headings 10
Tests 23
add
adf
arch
chow
coeffsum
coint
coint2
cusum
difftest
hausman
kpss
leverage
lmtest
meantest
normtest
omit
qlrtest
reset
restrict
runs
testuhat
vartest
vif
Dataset 16
append
data
dataset
delete
genr
info
labels
nulldata
open
rename
setinfo
setobs
setmiss
smpl
store
varlist
Estimation 28
ar
ar1
arbond
arima
equation
estimate
garch
gmm
hccm
heckit
hsk
lad
logistic
logit
mle
mpols
nls
ols
panel
poisson
probit
quantreg
system
tobit
tsls
var
vecm
wls
Graphs 6
boxplot
gnuplot
graph
plot
rmplot
scatters
Programming 14
break
elif
else
end
endif
endloop
foreign
function
if
include
loop
run
set
sscanf
Utilities 6
criteria
help
modeltab
pvalue
quit
shell
Statistics 11
corr
corrgm
freq
hurst
mahal
pca
pergm
spearman
summary
xcorrgm
xtab
Transformations 10
diff
discrete
dummify
lags
ldiff
logs
orthdev
rhodiff
sdiff
square
Printing 6
eqnprint
outfile
print
printf
sprintf
tabprint
Prediction 1
fcast

# add Tests

Argument:   lista-de-variáveis 
Options:    --vcv (mostrar matriz de covariância)
            --quiet (não mostrar estimativas para o modelo aumentado)
            --silent (não mostrar nada)
            --inst (acrescentar como instrumento, apenas para TSLS)
            --both (acrescentar tanto como regressor como instrumento, apenas para TSLS)
Examples:   add 5 7 9
            add xx yy zz --quiet

Tem que ser invocado após um comando de estimação. As variáveis na
lista-de-variáveis são acrescentadas ao modelo anterior e o novo modelo é
estimado. É apresentada uma estatística de significância conjunta,
juntamente com o seu p-value. O teste estatístico é o F no caso de
estimação por mínimos quadrados (OLS), ou qui-quadrado assimptótico de
Wald nos outros casos. Um p-value abaixo de 0,05 significa que os
coeficientes são conjuntamente significantes num nível de 5 porcento.

Se foi fornecida a opção --quiet os resultados apresentados ficam
confinados ao teste da significância conjunta das variáveis acrescentadas,
caso contrário, também serão mostradas as estimativas para o modelo
aumentado. Neste caso, a opção --vcv faz com que a matriz de covariâncias
dos coeficientes também seja apresentada. Se for usada a opção --silent,
não será mostrado nada; em em todo o caso, os resultados do teste podem
ser obtidos usando as variáveis especiais $test e $pvalue.

Se o modelo original foi estimado usando o método dos mínimos quadrados de
duas fases, ocorre uma situação ambígua: devem as novas variáveis serem
acrescentadas como sendo regressores, como instrumentos, ou como ambos? Isto
resolve-se do seguinte modo: por omissão as novas variáveis são
acrescentadas como regressores endógenos, mas se foi dada a opção --inst
elas são acrescentadas como instrumentos, ou se a opção --both está
presente elas são acrescentadas como regressores exógenos.

Menu path:    Janela do modelo, /Testes/Acrescentar variáveis

# adf Tests

Arguments:  ordem nome-de-variável 
Options:    --nc (teste sem constante)
            --c (apenas com constante)
            --ct (com constante e tendência)
            --ctt (com constante, tendência e quadrado da tendência)
            --seasonals (incluir variáveis sazonais auxiliares)
            --verbose (mostrar resultados da regressão)
            --quiet (não mostrar resultados da regressão)
            --difference (usar a primeira diferença da variável)
            --test-down (ordem 'lag' automática)
Examples:   adf 0 y
            adf 2 y --nc --c --ct
            adf 12 y --c --test-down
            See also jgm-1996.inp

Determina estatísticas para um conjunto de testes de Dickey-Fuller sobre a
variável especificada, com a hipótese nula de que a variável tem uma raiz
unitária. (Mas se a opção de diferenciação tiver sido dada, a primeira
diferença da variável é obtida, e a discussão abaixo deve ser
interpretada como sendo referente à variável transformada.)

Por omissão, são apresentadas três variantes do teste: uma baseada na
regressão contendo uma constante, uma usando uma constante e uma tendência
linear, e uma usando uma constante e uma tendência quadrática. Você pode
controlar as variantes que são apresentadas ao especificar uma ou mais
opções.

Em todos os casos a variável dependente é a primeira diferença da
variável especificada, y, e a variável independente chave é o primeiro
'lag' de y. O modelo é construido de modo a que o coeficiente do 'lag' de y
iguale 1 menos a raiz em questão. Por exemplo, o modelo com uma constante
pode ser escrito como

  (1 - L)y(t) = b0 + (1-a)y(t-1) + e(t)

Se a ordem de 'lag', k, é maior que 0, então k 'lags' da variável
dependente são incluidos no lado direito das regressões de teste, sujeitos
à seguinte qualificação. Se a opção --test-down foi dada, k é
considerada como sendo o 'lag' máximo e a ordem de 'lag' efectivamente
usada é obtida testando para baixo, de acordo com o seguinte algoritmo:

1. Estimar a regressão de Dickey-Fuller com k 'lags' da variável
   dependente.

2. O último 'lag' é significante? Se sim, executar o teste com com a ordem
   de 'lag', k. Senão, fazer k = k - 1; se k for igual a 0, executar o
   teste com a ordem de 'lag' 0, senão saltar para o passo 1.

No contexto do passo 2 acima, "significante" quer dizer que para o último
'lag', a estatística-t, que segue uma distribuição normal, tem um p-value
bilateral assimptótico menor ou igual a 0,10.

Os p-values para os testes de Dickey-Fuller baseiam-se em MacKinnon (1996).
O código relevante é incluído com a generosa permissão do autor.

Menu path:    /Variável/Teste de Dickey-Fuller aumentado

# append Dataset

Argument:   ficheiro-de-dados 

Abre um ficheiro de dados e acrescenta esse conteúdo ao conjunto de dados
actual, se os novos dados forem compatíveis. O programa tentará determinar
o formato do ficheiro de dados (nativo, texto simples, CVS, Gnumeric, Excel,
etc.).

Menu path:    /Ficheiro/Acrescentar dados

# ar Estimation

Arguments:  'lags' ; variável-dependente variáveis-independentes 
Option:     --vcv (mostrar matriz de covariância)
Example:    ar 1 3 4 ; y 0 x1 x2 x3

Determina estimativas para os parâmetros usando o procedimento iteractivo e
generalizado de Cochrane-Orcutt (ver a Secção 9.5 de Ramanathan, 2002). A
iteração termina quando os erros das somas de quadrados sucessivos não
difiram em mais que 0,005 porcento ou após 20 iterações.

"'lags'" é uma lista de 'lags' nos resíduos, terminada por um
ponto-e-vírgula. No exemplo acima o termo do erro é especificado como

  u(t) = rho(1)*u(t-1) + rho(3)*u(t-3) + rho(4)*u(t-4)

Menu path:    /Modelo/Série temporal/Estimação autoregressiva

# ar1 Estimation

Arguments:  depvar indepvars 
Options:    --hilu (use Hildreth-Lu procedure)
            --pwe (use Prais-Winsten estimator)
            --vcv (print covariance matrix)
            --no-corc (do not fine-tune results with Cochrane-Orcutt)
Examples:   ar1 1 0 2 4 6 7
            ar1 y 0 xlist --hilu --no-corc
            ar1 y 0 xlist --pwe

Computes feasible GLS estimates for a model in which the error term is
assumed to follow a first-order autoregressive process.

The default method is the Cochrane-Orcutt iterative procedure (see, for
example, Section 9.4 of Ramanathan, 2002). Iteration is terminated when
successive estimates of the autocorrelation coefficient do not differ by
more than 0.001 or after 20 iterations.

If the --hilu option is given, the Hildreth-Lu search procedure is used. The
results are then fine-tuned using the Cochrane-Orcutt method, unless the
--no-corc flag is specified. (The latter option is ignored if --hilu is not
specified.)

If the --pwe option is given, the Prais-Winsten estimator is used. This
involves an an iteration similar to Cochrane-Orcutt; the difference is that
while Cochrane-Orcutt discards the first observation, Prais-Winsten makes
use of it. See, for example, Chapter 13 of Greene's Econometric Analysis
(2000) for details.

Menu path:    /Model/Time series/Cochrane-Orcutt
Menu path:    /Model/Time series/Hildreth-Lu
Menu path:    /Model/Time series/Prais-Winsten

# arbond Estimation

Argument:   p [ q ] ; variável-dependente variáveis-independentes [ ; instrumentos ] 
Options:    --vcv (mostrar matriz de covariância)
            --two-step (executa estimação pelo Método Generalizado dos Momentos (GMM) de 2-fases)
            --time-dummies (acrescenta variáveis auxiliares tempo)
            --asymptotic (erros padrão assimptóticos)
Examples:   arbond 2 ; y Dx1 Dx2
            arbond 2 5 ; y Dx1 Dx2 ; Dx1
            arbond 1 ; y Dx1 Dx2 ; Dx1 GMM(x2,2,3)
            See also arbond91.inp

Executa a estimação de modelos de painel dinâmico (ou seja, modelos de
painel que contenham um ou mais 'lags' da variável dependente) recorrendo
ao método Método Generalizado dos Momentos (GMM) desenvolvido por Arellano
e Bond (1991).

O parâmetro p representa a ordem da autoregressão para a variável
dependente. O parâmetro opcional q indica o máximo 'lag' do nível da
variável dependente a ser usada como um instrumento. Se este argumento for
omitido, ou de valor 0, todos os 'lags' disponíveis são usados.

A variável dependente deve ser dada na forma de níveis; ela será
automaticamente diferenciada (pois este estimador usa diferenciação para
anular os efeitos individuais). As variáveis independentes não são
automaticamente diferenciadas; se você pretende usar diferenças (o que
acontece em geral para variáveis quantitativas, mas não será, por
exemplo, para variáveis auxiliares temporais), deve primeiro criar essas
diferenças e depois especificar estas como sendo regressoras.

O último campo (opcional) do comando serve para especificar instrumentos.
Se não for dado nenhum, então é assumido que todas as variáveis
independentes são estritamente exógenas. Se você especificar alguns
instrumentos, então deve incluir na lista quaisquer variáveis
independentes estritamente exógenas. Para regressores predeterminados,
você pode usar a função GMM para incluir uma gama de 'lags' especificada
no modo bloco-diagonal. Isto é ilustrado no terceiro exemplo acima. O
primeiro argumento de GMM é o nome da variável em questão, o segundo é o
mínimo 'lag' a ser usado como instrumento, e o terceiro é o máximo 'lag'.
Se o terceiro argumento for dado como 0, todos os 'lags' disponíveis são
usados.

Por omissão são apresentados os resultados da estimação 1-fase (com
erros padrão robustos). Opcionalmente, você pode escolher estimação de
2-fases. Em ambos os casos são efectuados testes de autocorrelação de
ordem 1 e 2, assim como o teste de sobre-identificação de Sargan e o teste
de Wald para a significância conjunta dos regressores. Note-se que este
modelo de diferenciação com autocorrelação de primeira-ordem não
invalida o modelo, mas que a autocorrelação de segunda-ordem não respeita
as assunções estatísticas presentes.

No caso da estimação de 2-fases, por omissão, os erros padrão são
determinados usando a correcção de amostra-finita sugerida por Windmeijer
(2005). Os erros padrão assimptóticos associados ao estimador de 2-fases
são em geral considerados como guias para inferência pouco fiáveis, mas
se por alguma razão os pretender ver, você pode usar a opção
--asymptotic para desligar a correcção de Windmeijer.

Se for dada a opção --time-dummies, são acrescentadas variáveis
auxiliares temporais aos regressores especificados. Para evitar
colinearidade perfeita com a constante, o número de auxiliares é uma
unidade a menos que o número máximo de períodos usados na estimação. As
auxiliares são introduzidas por níveis; se você deseja usar auxiliares de
tempo na forma de primeiras-diferenças, você terá que definir e
acrescentar essas variáveis manualmente.

Menu path:    /Modelo/Painel/Arellano-Bond

# arch Tests

Arguments:  ordem variável-dependente variáveis-independentes 
Example:    arch 4 y 0 x1 x2 x3

Testa o modelo em ARCH (Heterosquedicidade Condicional Autoregressiva) da
ordem de 'lag' especificada. Se a estatística de teste LM tiver um p-value
abaixo de 0,10, então a estimação ARCH também é executada. Se a
variância predita de qualquer observação na regressão auxiliar não for
positiva, então é usado o correspondente resíduo ao quadrado. Segue-se
uma estimação por Mínimos Quadrados com Pesos sobre o modelo original.

Ver também "garch".

Menu path:    Janela do modelo, /Testes/ARCH

# arima Estimation

Arguments:  p d q [ ; P D Q ] ; variável-dependente [ variáveis-independentes ] 
Options:    --verbose (mostrar detalhes das iterações)
            --vcv (mostrar matriz de covariância)
            --nc (não incluir uma constante)
            --conditional (usar verosimilhança máxima condicional)
            --x-12-arima (usar X-12-ARIMA para estimação)
Examples:   arima 1 0 2 ; y
            arima 2 0 2 ; y 0 x1 x2 --verbose
            arima 0 1 1 ; 0 1 1 ; y --nc

Se não for dada a lista de variáveis-independentes, é estimado um modelo
ARIMA (Média Móvel, Autoregressiva, Integrada) univariado. O valores
inteiros p, d e q representam respectivamente, a ordem autoregressiva (AR),
a ordem de diferenciação, e ordem da média móvel (MA). Estes valores
podem ser fornecidos na forma numérica, ou como nome de variáveis
escalares pré-existentes. Por exemplo, um valor de 1 em d, significa que a
primeira diferença da variável dependente deve ser obtida antes de estimar
os parâmetros ARMA.

Os valores inteiros opcionais,P, D e Q representam respectivamente, a
sazonalidade AR, a ordem para diferenciação de sazonalidade e a ordem de
sazonalidade MA. Estes são apenas aplicáveis se os dados tiverem uma
frequência superior a 1 (por exemplo, quadrimestral ou mensal). Mais uma
vez, estas ordens podem ser dadas na forma numérica ou como variáveis.

No caso univariado é incluído no modelo por omissão, um interceptor, mas
isto pode ser suprimido com a opção --nc. Se forem fornecidas
variáveis-independentes, o modelo passa a ser ARMAX; neste caso a constante
deve ser explicitamente incluída se você pretender um interceptor (tal
como no segundo exemplo acima).

Existe outra forma alternativa para este comando: se você não pretende
aplicar diferenciação (seja sazonal ou não-sazonal), você pode omitir
ambos os parâmetros d e D, em vez de entrar explicitamente zeros. Além
disso, arma é um sinónimo ou aliás para arima. Assim, por exemplo, o
comando seguinte é válido para especificar o modelo ARMA(2, 1):

	arma 2 1 ; y

O normal é usar a funcionalidade "nativa" gretl ARMA, com estimação de
Máxima Verosimilhança (ML) exacta (usando o filtro de Kalman). Outras
opções são: código nativo, ML condicional; X-12-ARIMA, ML exacta; e
X-12-ARIMA, ML condicional. (As últimas duas opções estão disponíveis
apenas se o programa X-12-ARIMA estiver instalado.) Para detalhes sobre
estas opções, veja por favor the Gretl User's Guide.

O valor AIC retornado em ligação com os modelos ARIMA é calculado
conforme a definição usada no programa X-12-ARIMA, nomeadamente

  AIC = -2L + 2k

onde L é o logaritmo da verosimilhança e k é o número total de
parâmetros estimados. Note-se que o programa X-12-ARIMA não produz
critérios de informação tal como o AIC quando a estimação é por ML
condicional.

A imagem da "frequência" apresentada em ligação com as raízes AR e MA é
valor lambda que resolve

  z = r * exp(i*2*pi*lambda)

onde z é a raiz em questão e r o seu módulo.

Menu path:    /Modelo/Série temporal/ARIMA
Other access: Menu de contexto da janela principal (selecção singular)

# boxplot Graphs

Argument:   lista-de-variáveis 
Option:     --notches (mostrar intervalo de 90 porcento para a mediana delimitado por entalhes)

Estes gráficos (criados por Tukey e Chambers) apresentam a distribuição
de uma variável. A caixa central contém os 50 porcento dos dados centrais,
i.e. está limitada pelos primeiro e terceiro quartis. Os "bigodes"
estendem-se até aos valores mínimo e máximo. É desenhada uma linha que
corta a caixa na mediana.

No caso de caixas com entalhes, os entalhes representam os limites do
intervalo de confiança para a mediana de cerca de 90 porcento. Isto é
obtido usando o método 'bootstrap'.

A seguir a cada variável indicada no comando caixa-com-bigodes, pode-se
acrescentar uma expressão Booleana para restringir a variável em questão.
Tem que se inserir um espaço entre o nome da variável ou número, e a
expressão. Suponha que você dispõe de valores de salários (salary) para
homens e mulheres, e que tem a variável auxiliar GENDER com valor 1 para
homens e 0 para mulheres. Nesse caso você podia ter gráficos
caixa-com-bigodes comparativos com a seguinte "lista-de-variáveis":

	salary (GENDER=1) salary (GENDER=0)

Alguns detalhes das caixas-com-bigodes do gretl podem ser controlados por
intermédio de um ficheiro (de texto simples) com o nome .boxplotrc. Para
mais detalhes sobre isto veja the Gretl User's Guide.

Menu path:    /Ver/Gráfico das variáveis/Caixa com bigodes

# break Programming

Sai de um ciclo. Este comando pode apenas ser usado dentro de um ciclo; ele
termina a execução de comandos e sai de dentro do ciclo (o mais interior).
Ver também "loop".

# chow Tests

Argument:   obs 
Examples:   chow 25
            chow 1988:1

Tem que se seguir a uma regressão de Mínimos Quadrados (OLS). Cria uma
variável auxiliar que é igual a 1 a partir do ponto especificado por obs
até ao final da amostra, caso contrário é 0, e cria também termos de
interacção entre esta variável auxiliar e as variáveis independentes
originais. É executada uma regressão aumentada que inclui estes termos e
é calculada a estatística F, considerando a regressão aumentada como não
restringida e a original como restringida. Esta estatística é apropriada
para testar a hipótese nula de não existência de quebra estrutural no
ponto de separação dado.

Menu path:    Janela do modelo, /Testes/Teste de Chow

# coeffsum Tests

Argument:   lista-de-variáveis 
Example:    coeffsum xt xt_1 xr_2
            See also restrict.inp

Tem que se seguir a uma regressão. Calcula a soma dos coeficientes nas
variáveis indicadas na lista-de-variáveis. Apresenta esta soma juntamente
com o seu erro padrão e o p-value para a hipótese nula de que a soma é
zero.

Note-se a diferença entre este teste e "omit", que testa a hipótese nula
de que os coeficientes num conjunto especificado de variáveis independentes
são todos iguais a zero.

Menu path:    Janela do modelo, /Testes/Soma de coeficientes

# coint Tests

Arguments:  ordem variável-dependente variáveis-independentes 
Options:    --nc (não incluir uma constante)
            --ct (incluir constante e tendência)
            --ctt (incluir constante e tendência quadrática)
            --skip-df (não efectuar testes DF nas variáveis individuais)
Examples:   coint 4 y x1 x2
            coint 0 y x1 x2 --ct --skip-df

O teste de cointegração Engle-Granger. O procedimento por omissão é: (1)
efectuar testes de Dickey-Fuller (DF) segundo a hipótese nula de que cada
variável listada tem uma raiz unitária; (2) estima a regressão de
cointegração; e (3)executar um teste DF sobre os resíduos da regressão
de cointegração. Se for dada a opção --skip-df, o passo (1) é omitido.

Se a ordem de 'lag' especificada é positiva, todos os testes Dickey-Fuller
usam essa ordem. Se a ordem for antecedida de um sinal menos, ela é
encarada como sendo o máximo 'lag' e a ordem efectivamente usada em cada
caso é obtida testando para baixo: ver o comando "adf" para detalhes.

Por omissão, a regressão de cointegração contém uma constante. Se você
deseja suprimir a constante, acrescente a opção --nc. Se você deseja
aumentar a lista de termos determinísticos na regressão de cointegração
com uma tendência linear ou quadrática, use a opção --ct ou --ctt. Estas
opções são mutualmente exclusivas.

Os P-values para este teste são baseados em MacKinnon (1996). O código
relevante é incluído com a generosa permissão do autor.

Menu path:    /Modelo/Série temporal/Testes de Cointegração/Engle-Granger

# coint2 Tests

Arguments:  ordem variável-dependente variáveis-independentes 
Options:    --nc (sem constante)
            --rc (constante restringida)
            --crt (constante e tendência restringida)
            --ct (constante e tendência não restringida)
            --seasonals (incluir auxiliares sazonais centradas)
            --quiet (apenas mostrar os testes)
            --verbose (mostrar detalhes das regressões auxiliares)
Examples:   coint2 2 y x
            coint2 4 y x1 x2 --verbose
            coint2 3 y x1 x2 --rc

Executa o teste de Johansen para a cointegração entre as variáveis
listadas para a dada ordem de 'lag'. Os valores críticos são determinados
usando a aproximação gamma de J. Doornik (Doornik, 1998). Para detalhes
sobre este teste ver o Capítulo 20 do livro de Hamilton, Time Series
Analysis (1994).

A inclusão de termos determinísticos no modelo é controlada por
intermédio das opções. Por omissão, se não tiver sido indicada nenhuma
opção, será incluída uma "constante não restringida", o que permite a
presença de um interceptor não-nulo nas relações cointegrantes assim
como uma tendência nos níveis das variáveis endógenas. Na literatura
derivada do trabalho de Johansen (ver por exemplo o livro dele de 1995) isto
é frequentemente referido como sendo o "caso 3". As primeiras quatro
opções apresentadas acima, que são mutualmente exclusivas, produzem
respectivamente os casos 1, 2, 4, e 5. O significado destes casos e os
critérios para seleccionar um caso estão explicados no the Gretl User's
Guide.

A opção --seasonals, que pode ser combinada com qualquer outra opção,
especifica a inclusão de um conjunto de variáveis auxiliares sazonais.
Esta opção apenas está disponível para dados trimestrais ou mensais.

A seguinte tabela serve como uma guia à interpretação dos resultados
apresentados pelo teste, num caso de 3-variáveis. H0 significa a hipótese
nula, H1 a hipótese alternativa, e c o número de relações cointegrantes.

                 Ordem    Teste Traço        Teste Lmax
                          H0     H1          H0     H1
                 ---------------------------------------
                  0      c = 0  c = 3       c = 0  c = 1
                  1      c = 1  c = 3       c = 1  c = 2
                  2      c = 2  c = 3       c = 2  c = 3
                 ---------------------------------------

Ver também o comando "vecm".

Menu path:    /Modelo/Série temporal/Testes de Cointegração/Johansen

# corr Statistics

Argument:   [ lista-de-variáveis ] 
Example:    corr y x1 x2 x3

Apresenta os coeficientes de correlação emparelhados das variáveis em
lista-de-variáveis, ou de todas as variáveis no conjunto de dados se não
for dada a lista-de-variáveis.

Menu path:    /Ver/Matriz de correlação
Other access: Menu de contexto da janela principal (selecção múltipla)

# corrgm Statistics

Arguments:  variável [ maxlag ] 
Example:    corrgm x 12

Apresenta os valores da função de autocorrelação para a variável, que
pode ser especificada por nome ou por número. Os valores são definidos
pela equação rho(u(t), u(t-s)) onde u_t é a t-ésima observação da
variável u e s é o número de "lags".

Também são apresentadas as autocorrelações parciais (obtidas segundo o
algoritmo de Durbin-Levinson): estas constituem a rede dos efeitos dos
"lags" intervenientes. O comando também produz o gráfico correlograma e
apresenta a estatística de teste Q de Box-Pierce, para a hipótese nula de
que a série temporal é "ruído branco": terá uma distribuição
qui-quadrado assimptótico com os graus de liberdade iguais ao número de
"lags" usados.

Se o valor maxlag for especificado o comprimento do correlograma fica
limitado a esse máximo número de "lags", senão o comprimento é
determinado automáticamente, como uma função da frequência dos dados e
do número de observações.

Menu path:    /Variável/Correlograma
Other access: Menu de contexto da janela principal (selecção singular)

# criteria Utilities

Arguments:  ess T k 
Example:    criteria 23.45 45 8

Determina o Critério de Informação de Akaike (AIC) e o Critério de
Informação Bayesiano de Schwarz (BIC), dados ess (erro da soma de
quadrados), o número de observações (T), e o número de coeficientes (k).
T, k, e ess podem ser valores numéricos ou nomes de variáveis préviamente
definidas.

O AIC é obtido segundo a formulação original de Akaike (1974),
nomeadamente

  AIC = -2L + 2k

onde L designa a verosimilhança logaritmica maximizada. O BIC é calculado
por

  BIC = -2L + k log T

Por favor consulte the Gretl User's Guide para mais pormenores.

# cusum Tests

Option:     --squares (executa o teste CUSUMSQ)

Tem que se seguir à estimação de um modelo por via de OLS. Executa o
teste CUSUM -- ou se for dada a opção --squares, o teste CUSUMSQ -- para a
estabilidade dos parâmetros. É obtida uma série temporal de erros de
predição um passo-à-frente, pela execução de séries de regressões: a
primeira regressão usa as primeiras k observações e é usada para gerar a
predição da variável dependente na observação k + 1; a segunda usa a
primeira predição para a observação k + 2, e por aí a diante (onde k é
o número de parâmetros no modelo original).

A soma acumulada dos erros de predição escalados, ou os quadrados desses
erros, é mostrada e apresentada em gráfico. A hipótese nula para a
estabilidade dos parâmetros é rejeitada ao nível de cinco porcento, se a
soma acumulada se desviar do intervalo de confiança de 95 porcento.

No caso do teste CUSUM, é também apresentada a estatística de teste t de
Harvey-Collier, para a hipótese nula da estabilidade dos parâmetros. Ver o
livro Econometric Analysis de Greene para mais detalhes. Para o teste
CUSUMSQ, o intervalo de confiança a 95 porcento é calculado de acordo com
o algoritmo apresentado por Edgerton e Wells (1994).

Menu path:    Janela do modelo, /Testes/Teste CUSUM(SQ)

# data Dataset

Argument:   lista-de-variáveis 

Reads the variables in lista-de-variáveis from a database (gretl, RATS 4.0
or PcGive), which must have been opened previously using the "open" command.
The data frequency and sample range may be established via the "setobs" and
"smpl" commands prior to using this command. Here is a full example:

	open macrodat.rat
	setobs 4 1959:1
	smpl ; 1999:4
	data GDP_JP GDP_UK

The commands above open a database named macrodat.rat, establish a quarterly
data set starting in the first quarter of 1959 and ending in the fourth
quarter of 1999, and then import the series named GDP_JP and GDP_UK.

If setobs and smpl are not specified in this way, the data frequency and
sample range are set using the first variable read from the database.

If the series to be read are of higher frequency than the working data set,
you may specify a compaction method as below:

	data (compact=average) LHUR PUNEW

The four available compaction methods are "average" (takes the mean of the
high frequency observations), "last" (uses the last observation), "first"
and "sum". If no method is specified, the default is to use the average.

Menu path:    /Ficheiro/Databases

# dataset Dataset

Arguments:  keyword parameters 
Examples:   dataset addobs 24
            dataset compact 1
            dataset compact 4 last
            dataset expand 12
            dataset transpose
            dataset sortby x1

Performs various operations on the data set as a whole, depending on the
given keyword, which must be addobs, compact, expand, transpose, sortby or
dsortby. Note: these actions are not available when the dataset is currently
subsampled by selection of cases on some Boolean criterion.

addobs: Must be followed by a positive integer. Adds the specified number of
extra observations to the end of the working dataset. This is primarily
intended for forecasting purposes. The values of most variables over the
additional range will be set to missing, but certain deterministic variables
are recognized and extended, namely, a simple linear trend and periodic
dummy variables.

compact: Must be followed by a positive integer representing a new data
frequency, which should be lower than the current frequency (for example, a
value of 4 when the current frequency is 12 indicates compaction from
monthly to quarterly). This command is available for time series data only;
it compacts all the series in the data set to the new frequency. A second
parameter may be given, namely one of sum, first or last, to specify,
respectively, compaction using the sum of the higher-frequency values,
start-of-period values or end-of-period values. The default is to compact by
averaging.

expand: Must be followed by a positive integer representing a new data
frequency, which should be higher than the current frequency. This command
is only available for annual or quarterly time series data. Annual data can
be expanded to quarterly or monthly; quarterly data can be expanded to
monthly. All the series in the data set are padded out to the new frequency
by repeating the existing values.

transpose: No additional parameter required. Transposes the current data
set. That is, each observation (row) in the current data set will be treated
as a variable (column), and each variable as an observation. This command
may be useful if data have been read from some external source in which the
rows of the data table represent variables.

sortby: One variable name is required; this variable is used as a sort key.
The observations on all variables in the dataset are re-ordered by
increasing value of the key variable. This command is available only for
undated data.

dsortby: Works as sortby except that the re-ordering is by decreasing value
of the key variable.

Menu path:    /Dados/Acrescentar observações
Menu path:    /Dados/Compactar datos
Menu path:    /Dados/Expandir datos
Menu path:    /Dados/Transpôr
Menu path:    /Dados/Ordenar

# delete Dataset

Argument:   [ lista-de-variáveis ] 

Removes the listed variables (given by name or number) from the dataset. Use
with caution: no confirmation is asked, and any variables with higher ID
numbers will be re-numbered.

If no lista-de-variáveis is given with this command, it deletes the last
(highest numbered) variable from the dataset.

Menu path:    Main window pop-up (single selection)

# diff Transformations

Argument:   lista-de-variáveis 

The first difference of each variable in lista-de-variáveis is obtained and
the result stored in a new variable with the prefix d_. Thus "diff x y"
creates the new variables

	d_x = x(t) - x(t-1)
	d_y = y(t) - y(t-1)

Menu path:    /Add/First differences of selected variables

# difftest Tests

Arguments:  var1 var2 
Options:    --sign (Sign test, the default)
            --rank-sum (Wilcoxon rank-sum test)
            --signed-rank (Wilcoxon signed-rank test)
            --verbose (print extra output)

Carries out a nonparametric test for a difference between two populations or
groups, the specific test depending on the option selected.

With the --sign option, the Sign test is performed. This test is based on
the fact that if two samples, x and y, are drawn randomly from the same
distribution, the probability that x_i > y_i, for each observation i, should
equal 0.5. The test statistic is w, the number of observations for which x_i
> y_i. Under the null hypothesis this follows the Binomial distribution with
parameters (n, 0.5), where n is the number of observations.

With the --rank-sum option, the Wilcoxon rank-sum test is performed. This
test proceeds by ranking the observations from both samples jointly, from
smallest to largest, then finding the sum of the ranks of the observations
from one of the samples. The two samples do not have to be of the same size,
and if they differ the smaller sample is used in calculating the rank-sum.
Under the null hypothesis that the samples are drawn from populations with
the same median, the probability distribution of the rank-sum can be
computed for any given sample sizes; and for reasonably large samples a
close Normal approximation exists.

With the --signed-rank option, the Wilcoxon signed-rank test is performed.
This is designed for matched data pairs such as, for example, the values of
a variable for a sample of individuals before and after some treatment. The
test proceeds by finding the differences between the paired observations,
x_i - y_i, ranking these differences by absolute value, then assigning to
each pair a signed rank, the sign agreeing with the sign of the difference.
One then calculates W_+, the sum of the positive signed ranks. As with the
rank-sum test, this statistic has a well-defined distribution under the null
that the median difference is zero, which converges to the Normal for
samples of reasonable size.

For the Wilcoxon tests, if the --verbose option is given then the ranking is
printed. (This option has no effect if the Sign test is selected.)

# discrete Transformations

Argument:   lista-de-variáveis 
Option:     --reverse (mark variables as continuous)

Marks each variable in lista-de-variáveis as being discrete. By default all
variables are treated as continuous; marking a variable as discrete affects
the way the variable is handled in frequency plots, and also allows you to
select the variable for the command "dummify".

If the --reverse flag is given, the operation is reversed; that is, the
variables in lista-de-variáveis are marked as being continuous.

Menu path:    /Variável/Edit attributes

# dummify Transformations

Argument:   lista-de-variáveis 
Options:    --drop-first (omit lowest value from encoding)
            --drop-last (omit highest value from encoding)

For any suitable variables in lista-de-variáveis, creates a set of dummy
variables coding for the distinct values of that variable. Suitable
variables are those that have been explicitly marked as discrete, or those
that take on a fairly small number of values all of which are "fairly round"
(multiples of 0.25).

By default a dummy variable is added for each distinct value of the variable
in question. For example if a discrete variable x has 5 distinct values, 5
dummy variables will be added to the data set, with names Dx_1, Dx_2 and so
on. The first dummy variable will have value 1 for observations where x
takes on its smallest value, 0 otherwise; the next dummy will have value 1
when x takes on its second-smallest value, and so on. If one of the option
flags --drop-first or --drop-last is added, then either the lowest or the
highest value of each variable is omitted from the encoding (which may be
useful for avoiding the "dummy variable trap").

This command can also be embedded in the context of a regression
specification. For example, the following line specifies a model where y is
regressed on the set of dummy variables coding for x. (Option flags cannot
be passed to "dummify" in this context.)

	ols y dummify(x)

# elif Programming

See "if".

# else Programming

See "if".

# end Programming

Ends a block of commands of some sort. For example, "end system" terminates
an equation "system".

# endif Programming

See "if".

# endloop Programming

Marks the end of a command loop. See "loop".

# eqnprint Printing

Argument:   [ -f filename ] 
Option:     --complete (Create a complete document)

Must follow the estimation of a model. Prints the estimated model in the
form of a LaTeX equation. If a filename is specified using the -f flag
output goes to that file, otherwise it goes to a file with a name of the
form equation_N.tex, where N is the number of models estimated to date in
the current session. See also "tabprint".

If the --complete flag is given, the LaTeX file is a complete document,
ready for processing; otherwise it must be included in a document.

Menu path:    Janela do modelo, /LaTeX

# equation Estimation

Arguments:  variável-dependente variáveis-independentes 
Example:    equation y x1 x2 x3 const

Specifies an equation within a system of equations (see "system"). The
syntax for specifying an equation within an SUR system is the same as that
for, e.g., "ols". For an equation within a Three-Stage Least Squares system
you may either (a) give an OLS-type equation specification and provide a
common list of instruments using the "instr" keyword (again, see "system"),
or (b) use the same equation syntax as for "tsls".

# estimate Estimation

Arguments:  systemname estimator 
Options:    --iterate (iterate to convergence)
            --no-df-corr (no degrees of freedom correction)
            --geomean (see below)
Examples:   estimate "Klein Model 1" method=fiml
            estimate Sys1 method=sur
            estimate Sys1 method=sur --iterate

Calls for estimation of a system of equations, which must have been
previously defined using the "system" command. The name of the system should
be given first, surrounded by double quotes if the name contains spaces. The
estimator, which must be one of "ols", "tsls", "sur", "3sls", "fiml" or
"liml", is preceded by the string method=.

If the system in question has had a set of restrictions applied (see the
"restrict" command), estimation will be subject to the specified
restrictions.

If the estimation method is "sur" or "3sls" and the --iterate flag is given,
the estimator will be iterated. In the case of SUR, if the procedure
converges the results are maximum likelihood estimates. Iteration of
three-stage least squares, however, does not in general converge on the
full-information maximum likelihood results. The --iterate flag is ignored
for other methods of estimation.

If the equation-by-equation estimators "ols" or "tsls" are chosen, the
default is to apply a degrees of freedom correction when calculating
standard errors. This can be suppressed using the --no-df-corr flag. This
flag has no effect with the other estimators; no degrees of freedom
correction is applied in any case.

By default, the formula used in calculating the elements of the
cross-equation covariance matrix is

  sigma(i,j) = u(i)' * u(j) / T

If the --geomean flag is given, a degrees of freedom correction is applied:
the formula is

  sigma(i,j) = u(i)' * u(j) / sqrt((T - ki) * (T - kj))

where the ks denote the number of independent parameters in each equation.

# fcast Prediction

Arguments:  [ startobs endobs ] fitvar 
Options:    --dynamic (create dynamic forecast)
            --static (create static forecast)
Examples:   fcast 1997:1 2001:4 f1
            fcast fit2

Must follow an estimation command. Forecasts are generated for the specified
range (or the largest possible range if no startobs and endobs are given)
and the values saved as fitvar, which can be printed, graphed, or plotted.
The right-hand side variables are those in the original model. There is no
provision to substitute other variables. If an autoregressive error process
is specified the forecast incorporates the predictable fraction of the error
process.

The choice between a static and a dynamic forecast applies only in the case
of dynamic models, with an autoregressive error process or including one or
more lagged values of the dependent variable as regressors. See "fcasterr"
for more details.

Menu path:    Janela do modelo, /Analysis/Forecasts

# foreign Programming

Argument:   [ language-spec ] 
Options:    --send-data (pre-load the current dataset)
            --quiet (suppress output from foreign program)

This command opens a special mode in which commands to be executed by
another program are accepted. You exit this mode with end foreign; at this
point the stacked commands are executed.

At present the only "foreign" program supported in this way is GNU R, and
the only acceptable language-spec is language=R (which is implicit, and may
be omitted). In future other programs may be supported.

See the Gretl User's Guide for details and examples.

# freq Statistics

Argument:   var 
Options:    --quiet (suppress printing of histogram)
            --gamma (test for gamma distribution)

With no options given, displays the frequency distribution for var (given by
name or number) and shows the results of the Doornik-Hansen chi-square test
for normality.

If the --quiet option is given, the histogram is not shown. If the --gamma
option is given, the test for normality is replaced by Locke's nonparametric
test for the null hypothesis that the variable follows the gamma
distribution; see Locke (1976), Shapiro and Chen (2001).

In interactive mode a graph of the distribution is displayed.

Menu path:    /Variável/Frequency distribution

# function Programming

Argument:   fnname 

Opens a block of statements in which a function is defined. This block must
be closed with end function. Please see the Gretl User's Guide for details.

# garch Estimation

Arguments:  p q ; variável-dependente [ variáveis-independentes ] 
Options:    --robust (robust standard errors)
            --verbose (mostrar detalhes das iterações)
            --vcv (mostrar matriz de covariância)
            --arma-init (initial variance parameters from ARMA)
Examples:   garch 1 1 ; y
            garch 1 1 ; y 0 x1 x2 --robust

Estimates a GARCH model (GARCH = Generalized Autoregressive Conditional
Heteroskedasticity), either a univariate model or, if
variáveis-independentes are specified, including the given exogenous
variables. The integer values p and q (which may be given in numerical form
or as the names of pre-existing scalar variables) represent the lag orders
in the conditional variance equation:

  h(t) = a(0) + sum(i=1 to q) a(i)*u(t-i)^2 + sum(j=1 to p) b(j)*h(t-j)

The gretl GARCH algorithm is basically that of Fiorentini, Calzolari and
Panattoni (1996), used by kind permission of Professor Fiorentini.

Several variant estimates of the coefficient covariance matrix are available
with this command. By default, the Hessian is used unless the --robust
option is given, in which case the QML (White) covariance matrix is used.
Other possibilities (e.g. the information matrix, or the
Bollerslev-Wooldridge estimator) can be specified using the "set" command.

By default, the estimates of the variance parameters are initialized using
the unconditional error variance from initial OLS estimation for the
constant, and small positive values for the coefficients on the past values
of the squared error and the error variance. The flag --arma-init calls for
the starting values of these parameters to be set using an initial ARMA
model, exploiting the relationship between GARCH and ARMA set out in Chapter
21 of Hamilton's Time Series Analysis. In some cases this may improve the
chances of convergence.

The GARCH residuals and estimated conditional variance can be retrieved as
$uhat and $h respectively. For example, to get the conditional variance:

	genr ht = $h

Menu path:    /Modelo/Série temporal/GARCH

# genr Dataset

Arguments:  newvar = formula 

Creates new variables, usually through transformations of existing
variables. See also "diff", "logs", "lags", "ldiff", "multiply", "sdiff" and
"square" for shortcuts. In the context of a genr formula, existing variables
must be referenced by name, not ID number. The formula should be a
well-formed combination of variable names, constants, operators and
functions (described below). Note that further details on some aspects of
this command can be found in the Gretl User's Guide.

This command may yield either a series or a scalar result. For example, the
formula x2 = x * 2 naturally yields a series if the variable x is a series
and a scalar if x is a scalar. The formulae x = 0 and mx = mean(x) naturally
return scalars. Under some circumstances you may want to have a scalar
result expanded into a series or vector. You can do this by using series as
an "alias" for the genr command. For example, series x = 0 produces a series
all of whose values are set to 0. You can also use scalar as an alias for
genr. It is not possible to coerce a vector result into a scalar, but use of
this keyword indicates that the result should be a scalar: if it is not, an
error occurs.

When a formula yields a series or vector result, the range over which the
result is written to the target variable depends on the current sample
setting. It is possible, therefore, to define a series piecewise using the
smpl command in conjunction with genr.

Supported arithmetical operators are, in order of precedence: ^
(exponentiation); *, / and % (modulus or remainder); + and -.

The available Boolean operators are (again, in order of precedence): !
(negation), && (logical AND), || (logical OR), >, <, =, >= (greater than or
equal), <= (less than or equal) and != (not equal). The Boolean operators
can be used in constructing dummy variables: for instance (x > 10) returns 1
if x > 10, 0 otherwise.

Built-in constants are pi and NA. The latter is the missing value code: you
can initialize a variable to the missing value with scalar x = NA.

Supported functions fall into these groups:

  Standard mathematical functions: abs, exp, int (integer part), ln (natural
  logarithm: log is a synonym), log10 (log to the base 10), log2 (log to the
  base 2), sqrt. All of these take a single argument, which may be either a
  series or a scalar.

  Trigonometric functions: cos, sin, tan and atan (arc tangent). These
  functions also take a single series or scalar argument.

  Standard statistical functions taking a single argument and yielding a
  scalar result: max (maximum value in a series), min (minimum value in
  series), mean (arithmetic mean), median, var (variance), sd (standard
  deviation), sst (sum of squared deviations from the mean), sum, gini (Gini
  coefficient).

  Statistical functions taking one series as argument and yielding a series
  or vector result: sort (sort a series in ascending order of magnitude),
  dsort (sort in descending order), cum (cumulate, or running sum). For
  panel data only, the functions pmean and psd take a single series as
  argument and return series containing, respectively, the means and
  standard deviations of the variable for each of the cross-sectional units.
  See the Gretl User's Guide for details.

  Statistical functions taking two series as arguments and yielding a scalar
  result: cov (covariance), corr (correlation coefficient).

  Special statistical functions: pvalue, cdf and critical (see below), cnorm
  (standard normal CDF), dnorm (standard normal PDF), qnorm (standard normal
  quantiles, or inverse of standard normal PDF), resample (resample a series
  with replacement, for bootstrap purposes), hpfilt (Hodrick-Prescott
  filter: this function returns the "cycle" component of the series), bkfilt
  (Baxter-King bandpass filter).

  Time-series functions: diff (first difference), ldiff (log-difference, or
  first difference of natural logs), sdiff (seasonal difference), fracdiff
  (fractional difference). To generate lags of a variable x, use the syntax
  "x(-N)", where N represents the desired lag length; to generate leads, use
  "x(+N)".

  Dataset functions yielding a series: misszero (replaces the missing
  observation code in a given series with zeros); zeromiss (the inverse
  operation to misszero); missing (at each observation, 1 if the argument
  has a missing value, 0 otherwise); ok (the opposite of missing).

  Dataset functions yielding a scalar: nobs (gives the number of valid
  observations in a data series), firstobs (gives the 1-based observation
  number of the first non-missing value in a series), lastobs (observation
  number of the last non-missing observation in a series).

  Pseudo-random numbers: uniform, normal, chisq, student, binomial and
  poisson. The first two functions do not take an argument and should be
  written with empty parentheses: uniform(), normal(). They create
  pseudo-random series drawn from the uniform (0-1) and standard normal
  distributions respectively. The next two take a single argument, namely
  the (integer) degrees of freedom; they generate drawings from the
  Chi-square and Student's t distributions respectively. The binomial
  function takes two parameters: an integer number of trials and a "success"
  probability. The poisson function takes a single argument, the mean, which
  may be either a scalar or a series. Uniform series, which form the basis
  for the more complex distributions, are generated using the Mersenne
  Twister; for normal series the method of Box and Muller (1958) is used,
  taking input from the Mersenne Twister. Ver também o comando "set"
  command, seed option.

With a few exceptions as noted, all the above functions take as their single
argument either the name of a variable or an expression that evaluates to a
variable (e.g. "ln((x1+x2)/2)").

The pvalue, critical and cdf functions take, as their first parameter, a
one-character code for the distribution of interest:

  "z" or "N": Normal;

  "t": Student's t;

  "X": chi-square;

  "F": Snedecor;

  "G": Gamma (only "pvalue" and "cdf");

  "B": Binomial (only "pvalue" and "cdf");

  "D": Bivariate normal (only "cdf").

The pvalue function takes the same arguments as the "pvalue" command, but in
this context commas should be placed between the arguments. It returns a
one-tailed, right-hand p-value, that is, the integral of the given density
function from the specified value to plus infinity.

The cdf function also takes the same arguments as the "pvalue" command, but
it returns the complementary value, that is, the integral of the relevant
density function from its lower limit (either minus infinity or zero) to the
specified value. For the bivariate normal distribution, three arguments must
be given: x, y and the correlation coefficient rho, in this order. For
additional discussion of these functions see the Gretl User's Guide.

The critical function returns the critical value for a specified probability
distribution and a specified proportion in the right-hand tail (with
specified degrees of freedom where applicable). The last parameter is the
right-hand tail proportion. If the first parameter is "t" or "X", a second
parameter must give the degrees of freedom. For the F distribution, the
second and third parameters must give the numerator and denominator degrees
of freedom.

Here are some examples of use of the pvalue and critical functions (spaces
between the arguments are optional):

	genr p1 = pvalue(z, 2.2)
	genr p2 = pvalue(X, 3, 5.67)
	genr p3 = cdf(D, -1, 1, 0.25)
	genr c1 = critical(t, 20, 0.025)
	genr c2 = critical(F, 4, 48, 0.05)

The functions relating to the standard normal density work thus, for a given
argument x: cnorm returns the area under the standard normal density
function integrated from minus infinity to x; dnorm returns the standard
normal density evaluated at x; and qnorm returns the z such that the area
under the standard normal density, integrated from minus infinity to z,
equals x.

The fracdiff function takes two arguments: the name of the series and a
fraction, in the range -1 to 1.

Besides the operators and functions noted above there are some special uses
of "genr":

  "genr time" creates a time trend variable (1,2,3,...) called "time". "genr
  index" does the same thing except that the variable is called index.

  "genr dummy" creates dummy variables up to the periodicity of the data.
  For example, in the case of quarterly data (periodicity 4), the program
  creates dummy_1 = 1 for first quarter and 0 in other quarters, dummy_2 = 1
  for the second quarter and 0 in other quarters, and so on.

  "genr unitdum" and "genr timedum" create sets of special dummy variables
  for use with panel data. The first codes for the cross-sectional units and
  the second for the time period of the observations.

Various internal variables defined in the course of running a regression can
be retrieved using "genr", as follows:

  $ess: error sum of squares

  $rsq: unadjusted R-squared

  $T: number of observations used

  $df: degrees of freedom

  $ncoeff: total number of estimated coefficients

  $trsq: TR-squared (sample size times R-squared)

  $sigma: standard error of residuals

  $aic: Akaike Information Criterion

  $bic: Schwarz's Bayesian Information Criterion

  $hqc: Hannan-Quinn Criterion

  $lnl: log-likelihood (where applicable)

  $coeff(var): estimated coefficient for variable var

  $stderr(var): estimated standard error for variable var

  $rho(i): ith order autoregressive coefficient for residuals

  $vcv(x1,x2): estimated covariance between coefficients for the variables
  x1 and x2

Note: In the command-line program, "genr" commands that retrieve
model-related data always reference the model that was estimated most
recently. This is also true in the GUI program, if one uses "genr" in the
"gretl console" or enters a formula using the "Define new variable" option
under the Variable menu in the main window. With the GUI, however, you have
the option of retrieving data from any model currently displayed in a window
(whether or not it's the most recent model). You do this under the "Model
data" menu in the model's window.

The internal series $uhat and $yhat hold, respectively, the residuals and
fitted values from the last regression. For GARCH models, the conditional
variance is held in the internal variable $h.

Three "internal" dataset variables are available: $nobs holds the number of
observations in the current sample range (which may or may not equal the
value of $T, the number of observations used in estimating the last model);
$nvars holds the number of variables in the dataset (including the
constant); and $pd holds the frequency or periodicity of the data (e.g. 4
for quarterly data).

Two special internal scalars, $test and $pvalue, hold respectively the value
and the p-value of the test statistic that was generated by the last
explicit hypothesis-testing command, if any (e.g. chow). Please see the
Gretl User's Guide for details on this.

The variable t serves as an index of the observations. For instance genr dum
= (t=15) will generate a dummy variable that has value 1 for observation 15,
0 otherwise. The variable obs is similar but more flexible: you can use this
to pick out particular observations by date or name. For example, genr d =
(obs>1986:4) or genr d = (obs="CA"). The last form presumes that the
observations are labeled; the label must be put in double quotes.

Scalar values can be pulled from a series in the context of a genr formula,
using the syntax varname[obs]. The obs value can be given by number or date.
Examples: x[5], CPI[1996:01]. For daily data, the form YYYY/MM/DD should be
used, e.g. ibm[1970/01/23].

An individual observation in a series can be modified via genr. To do this,
a valid observation number or date, in square brackets, must be appended to
the name of the variable on the left-hand side of the formula. For example,
genr x[3] = 30 or genr x[1950:04] = 303.7.

Here are a couple of tips on dummy variables:

  Suppose x is coded with values 1, 2, or 3 and you want three dummy
  variables, d1 = 1 if x = 1, 0 otherwise, d2 = 1 if x = 2, and so on. To
  create these, use the commands:

	    genr d1 = (x=1)
	    genr d2 = (x=2)
	    genr d3 = (x=3)

  To create z = max(x,y) do

	    genr d = x>y
	    genr z = (x*d)+(y*(1-d))

  Formula                Comment
  -------                -------
  y = x1^3               x1 cubed
  y = ln((x1+x2)/x3)     
  z = x>y                z(t) = 1 if x(t) > y(t), otherwise 0
  y = x(-2)              x lagged 2 periods
  y = x(+2)              x led 2 periods
  y = diff(x)            y(t) = x(t) - x(t-1)
  y = ldiff(x)           y(t) = log x(t) - log x(t-1), the instantaneous rate 
                         of growth of x
  y = sort(x)            sorts x in increasing order and stores in y
  y = dsort(x)           sort x in decreasing order
  y = int(x)             truncate x and store its integer value as y
  y = abs(x)             store the absolute values of x
  y = sum(x)             sum x values excluding missing -999 entries
  y = cum(x)             cumulation: y(t) = the sum from s=1 to s=t of x(s) 
  aa = $ess              set aa equal to the Error Sum of Squares from last 
                         regression
  x = $coeff(sqft)       grab the estimated coefficient on the variable sqft 
                         from the last regression
  rho4 = $rho(4)         grab the 4th-order autoregressive coefficient from 
                         the last model (presumes an ar model)
  cvx1x2 = $vcv(x1, x2)  grab the estimated coefficient covariance of vars x1 
                         and x2 from the last model
  foo = uniform()        uniform pseudo-random variable in range 0-1
  bar = 3 * normal()     normal pseudo-random variable, mu = 0, sigma = 3
  samp = ok(x)           = 1 for observations where x is not missing.

Menu path:    /Variável/Define new variable
Other access: Menu de contexto da janela principal

# gmm Estimation

Options:    --two-step (two step estimation)
            --iterate (iterated GMM)
            --vcv (print covariance matrix)
            --verbose (mostrar detalhes das iterações)

Performs Generalized Method of Moments (GMM) estimation using the BFGS
(Broyden, Fletcher, Goldfarb, Shanno) algorithm. You must specify one or
more commands for updating the relevant quantities (tyically, GMM
residuals), one or more sets of orthogonality conditions, an initial matrix
of weights, and a listing of the parameters to be estimated, all enclosed
between the tags gmm and end gmm.

Please see the Gretl User's Guide for details on this command. Here we just
illustrate with a simple example.

	gmm e = y - X*b
	  orthog e ; W
	  weights V
	  params b
	end gmm

In the example above we assume that y and X are data matrices, b is an
appropriately sized vector of parameter values, W is a matrix of
instruments, and V is a suitable matrix of weights. The statement

	orthog e ; W

indicates that the residual vector e is in principle orthogonal to each of
the instruments composing the columns of W.

Menu path:    /Model/GMM

# gnuplot Graphs

Arguments:  yvars xvar [ dumvar ] 
Options:    --with-lines (use lines, not points)
            --with-impulses (use vertical lines)
            --suppress-fit (don't show fitted line)
            --linear-fit (show least squares fit)
            --inverse-fit (show inverse fit)
            --quadratic-fit (show quadratic fit)
            --loess-fit (show loess fit)
            --dummy (see below)
Examples:   gnuplot y1 y2 x
            gnuplot x time --with-lines
            gnuplot wages educ gender --dummy

Without the --dummy option, the yvars are graphed against xvar. With
--dummy, yvar is graphed against xvar with the points shown in different
colors depending on whether the value of dumvar is 1 or 0.

The time variable behaves specially: if it does not already exist then it
will be generated automatically.

In interactive mode the result is displayed immediately. In batch mode a
gnuplot command file is written, with a name on the pattern gpttmpN.plt,
starting with N = 01. The actual plots may be generated later using gnuplot
(under MS Windows, wgnuplot).

The various "fit" options are applicable only in the case of a bivariate
scatterplot. The default behavior is to show the OLS fitted line if and only
if the slope coefficient is significant at the 10 percent level. If the
suppress option is given, no fitted line is shown. If the linear option is
given, the OLS line is shown regardless of whether or not it is significant.
The other options -- inverse, quadratic and loess -- produce respectively an
inverse fit (regression of y on 1/x), a quadratic fit, or a loess fit. Loess
(also sometimes called "lowess") is a robust locally weighted regression.

A further option to this command is available: following the specification
of the variables to be plotted and the option flag (if any), you may add
literal gnuplot commands to control the appearance of the plot (for example,
setting the plot title and/or the axis ranges). These commands should be
enclosed in braces, and each gnuplot command must be terminated with a
semi-colon. A backslash may be used to continue a set of gnuplot commands
over more than one line. Here is an example of the syntax:

{ set title 'My Title'; set yrange [0:1000]; }

Menu path:    /View/Graph specified vars
Other access: Menu de contexto da janela principal, graph button on toolbar

# graph Graphs

Arguments:  yvars xvar 
Option:     --tall (use 40 rows)

ASCII graphics. The yvars (which may be given by name or number) are graphed
against xvar using ASCII symbols. The --tall flag will produce a graph with
40 rows and 60 columns. Without it, the graph will be 20 by 60 (for screen
output). See also "gnuplot".

# hausman Tests

This test is available only after estimating an OLS model using panel data
(see also "setobs"). It tests the simple pooled model against the principal
alternatives, the fixed effects and random effects models.

The fixed effects model allows the intercept of the regression to vary
across the cross-sectional units. An F-test is reported for the null
hypotheses that the intercepts do not differ. The random effects model
decomposes the residual variance into two parts, one part specific to the
cross-sectional unit and the other specific to the particular observation.
(This estimator can be computed only if the number of cross-sectional units
in the data set exceeds the number of parameters to be estimated.) The
Breusch-Pagan LM statistic tests the null hypothesis that the pooled OLS
estimator is adequate against the random effects alternative.

The pooled OLS model may be rejected against both of the alternatives, fixed
effects and random effects. Provided the unit- or group-specific error is
uncorrelated with the independent variables, the random effects estimator is
more efficient than the fixed effects estimator; otherwise the random
effects estimator is inconsistent and the fixed effects estimator is to be
preferred. The null hypothesis for the Hausman test is that the
group-specific error is not so correlated (and therefore the random effects
model is preferable). A low p-value for this test counts against the random
effects model and in favor of fixed effects.

Menu path:    Janela do modelo, /Testes/Panel diagnostics

# hccm Estimation

Arguments:  variável-dependente variáveis-independentes 
Option:     --vcv (mostrar matriz de covariância)

Heteroskedasticity-Consistent Covariance Matrix: this command runs a
regression where the coefficients are estimated via the standard OLS
procedure, but the standard errors of the coefficient estimates are computed
in a manner that is robust in the face of heteroskedasticity, namely using
the MacKinnon-White "jackknife" procedure.

# heckit Estimation

Arguments:  variável-dependente variáveis-independentes ; selection equation 
Options:    --two-step (perform two-step estimation)
            --vcv (print covariance matrix)
            --verbose (print extra output)
Example:    heckit y 0 x1 x2 ; ys 0 x3 x4

Heckman-type selection model. In the specification, the list before the
semicolon represents the outcome equation, and the second list represents
the selection equation. The dependent variable in the selection equation (ys
in the example above) must be a binary variable.

By default, the parameters are estimated by maximum likelihood. The
covariance matrix of the parameters is computed using the negative inverse
of the Hessian. If two-step estimation is desired, use the --two-step
option. In this case, the covariance matrix of the parameters of the outcome
equation is appropriately adjusted as per Heckman (1979).

FIXME this entry needs to be completed.

Menu path:    /Model/Nonlinear models/Heckit

# help Utilities

Gives a list of available commands. "help" command describes command (e.g.
"help smpl"). You can type "man" instead of "help" if you like.

Menu path:    /Help

# hsk Estimation

Arguments:  variável-dependente variáveis-independentes 
Option:     --vcv (mostrar matriz de covariância)

An OLS regression is run and the residuals are saved. The logs of the
squares of these residuals then become the dependent variable in an
auxiliary regression, on the right-hand side of which are the original
independent variables plus their squares. The fitted values from the
auxiliary regression are then used to construct a weight series, and the
original model is re-estimated using weighted least squares. This final
result is reported.

The weight series is formed as 1/sqrt(exp(y*)), where y* denotes the fitted
values from the auxiliary regression.

Menu path:    /Model/Other linear models/Heteroskedasticity corrected

# hurst Statistics

Argument:   nome-de-variável 

Calculates the Hurst exponent (a measure of persistence or long memory) for
a time-series variable having at least 128 observations.

The Hurst exponent is discussed by Mandelbrot. In theoretical terms it is
the exponent, H, in the relationship

  RS(x) = an^H

where RS is the "rescaled range" of the variable x in samples of size n and
a is a constant. The rescaled range is the range (maximum minus minimum) of
the cumulated value or partial sum of x over the sample period (after
subtraction of the sample mean), divided by the sample standard deviation.

As a reference point, if x is white noise (zero mean, zero persistence) then
the range of its cumulated "wandering" (which forms a random walk), scaled
by the standard deviation, grows as the square root of the sample size,
giving an expected Hurst exponent of 0.5. Values of the exponent
significantly in excess of 0.5 indicate persistence, and values less than
0.5 indicate anti-persistence (negative autocorrelation). In principle the
exponent is bounded by 0 and 1, although in finite samples it is possible to
get an estimated exponent greater than 1.

In gretl, the exponent is estimated using binary sub-sampling: we start with
the entire data range, then the two halves of the range, then the four
quarters, and so on. For sample sizes smaller than the data range, the RS
value is the mean across the available samples. The exponent is then
estimated as the slope coefficient in a regression of the log of RS on the
log of sample size.

Menu path:    /Variável/Hurst exponent

# if Programming

Flow control for command execution. Three sorts of construction are
supported, as follows.

	# simple form
	if condition
	   commands
	endif
            
	# two branches
	if condition
	   commands1
	else
	   commands2
	endif
            
	# three or more branches
	if condition1
	   commands1
	elif condition2
	   commands2
	else
	   commands3
	endif
            
"condition" must be a Boolean expression, for the syntax of which see
"genr". More than one "elif" block may be included. In addition, if ...
endif blocks may be nested.

# include Programming

Argument:   inputfile 
Examples:   include myfile.inp
            include sols.gfn

Intended for use in a command script, primarily for including definitions of
functions. Executes the commands in inputfile then returns control to the
main script. To include a packaged function, be sure to include the filename
extension.

See also "run".

# info Dataset

Prints out any supplementary information stored with the current datafile.

Menu path:    /Data/Read info
Other access: Data browser windows

# kpss Tests

Arguments:  order varlist 
Options:    --trend (include a trend)
            --verbose (print regression results)
            --quiet (suppress printing of results)
            --difference (use first difference of variable)
Examples:   kpss 8 y
            kpss 4 x1 --trend

Computes the KPSS test (Kwiatkowski, Phillips, Schmidt and Shin, 1992) for
stationarity, for each of the specified variables (or their first
difference, if the --difference option is selected). The null hypothesis is
that the variable in question is stationary, either around a level or, if
the --trend option is given, around a deterministic linear trend.

The order argument determines the size of the window used for Bartlett
smoothing. If the --verbose option is chosen the results of the auxiliary
regression are printed, along with the estimated variance of the random walk
component of the variable.

Menu path:    /Variable/KPSS test

# labels Dataset

Prints out the informative labels for any variables that have been generated
using "genr", and any labels added to the data set via the GUI.

# lad Estimation

Arguments:  depvar indepvars 
Option:     --vcv (print covariance matrix)

Calculates a regression that minimizes the sum of the absolute deviations of
the observed from the fitted values of the dependent variable. Coefficient
estimates are derived using the Barrodale-Roberts simplex algorithm; a
warning is printed if the solution is not unique.

Standard errors are derived using the bootstrap procedure with 500 drawings.
The covariance matrix for the parameter estimates, printed when the --vcv
flag is given, is based on the same bootstrap.

Menu path:    /Model/Robust estimation/Least Absolute Deviation

# lags Transformations

Variants:   lags varlist
            lags order ; varlist
Examples:   lags x y
            lags 12 ; x y

Creates new variables which are lagged values of each of the variables in
varlist. By default the number of lagged variables equals the periodicity of
the data. For example, if the periodicity is 4 (quarterly), the command
"lags x" creates

	x_1 = x(t-1)
	x_2 = x(t-2)
	x_3 = x(t-3)
	x_4 = x(t-4)

The number of lags created can be controlled by the optional first
parameter.

Menu path:    /Add/Lags of selected variables

# ldiff Transformations

Argument:   varlist 

The first difference of the natural log of each variable in varlist is
obtained and the result stored in a new variable with the prefix ld_. Thus
"ldiff x y" creates the new variables

	ld_x = log(x) - log(x(-1))
	ld_y = log(y) - log(y(-1))

Menu path:    /Add/Log differences of selected variables

# leverage Tests

Option:     --save (save variables)

Must immediately follow an "ols" command. Calculates the leverage (h, which
must lie in the range 0 to 1) for each data point in the sample on which the
previous model was estimated. Displays the residual (u) for each observation
along with its leverage and a measure of its influence on the estimates,
u*h/(1-h). "Leverage points" for which the value of h exceeds 2k/n (where k
is the number of parameters being estimated and n is the sample size) are
flagged with an asterisk. For details on the concepts of leverage and
influence see Davidson and MacKinnon (1993, Chapter 2).

DFFITS values are also shown: these are "studentized residuals" (predicted
residuals divided by their standard errors) multiplied by sqrt[h/(1 - h)].
For a discussion of studentized residuals and DFFITS see G. S. Maddala,
Introduction to Econometrics, chapter 12; also Belsley, Kuh and Welsch
(1980).

Briefly, a "predicted residual" is the difference between the observed value
of the dependent variable at observation t, and the fitted value for
observation t obtained from a regression in which that observation is
omitted (or a dummy variable with value 1 for observation t alone has been
added); the studentized residual is obtained by dividing the predicted
residual by its standard error.

If the --save flag is given with this command, then the leverage, influence
and DFFITS values are added to the current data set.

Menu path:    Model window, /Tests/Influential observations

# lmtest Tests

Argument:   [ order ] 
Options:    --logs (non-linearity, logs)
            --autocorr (serial correlation)
            --arch (ARCH)
            --squares (non-linearity, squares)
            --white (heteroskedasticity, White's test)
            --panel (heteroskedasticity, groupwise)
            --quiet (don't print auxiliary regression)

Must immediately follow an "ols" command. Depending on the options given,
this command carries out some combination of the following: Lagrange
Multiplier tests for nonlinearity (logs and squares); White's test for
heteroskedasticity; the LMF test for serial correlation (see Kiviet, 1986);
and a test for ARCH (Autoregressive Conditional Heteroskedasticity, see also
the "arch" command).

The optional order argument is relevant only in case the --autocorr or
--arch options are selected. The default is to run these tests using a lag
order equal to the periodicity of the data, but this can be adjusted by
supplying a specific lag order.

The --panel option is available only when the model is estimated on panel
data: in this case a test for groupwise heteroskedasticity is performed
(that is, for a differing error variance across the cross-sectional units).

By default, the program prints the auxiliary regression on which the test
statistic is based. This may be suppressed by using the --quiet flag.

Menu path:    Janela do modelo, /Tests

# logistic Estimation

Arguments:  variável-dependente variáveis-independentes [ ymax=value ] 
Option:     --vcv (mostrar matriz de covariância)
Examples:   logistic y const x
            logistic y const x ymax=50

Logistic regression: carries out an OLS regression using the logistic
transformation of the dependent variable,

  log(y/(y* - y))

The dependent variable must be strictly positive. If it is a decimal
fraction, between 0 and 1, the default is to use a y* value (the asymptotic
maximum of the dependent variable) of 1. If the dependent variable is a
percentage, between 0 and 100, the default y* is 100.

If you wish to set a different maximum, use the optional ymax=value syntax
following the list of regressors. The supplied value must be greater than
all of the observed values of the dependent variable.

The fitted values and residuals from the regression are automatically
transformed using

  y = y* / (1 + exp(-x))

where x represents either a fitted value or a residual from the OLS
regression using the transformed dependent variable. The reported values are
therefore comparable with the original dependent variable.

Note that if the dependent variable is binary, you should use the "logit"
command instead.

Menu path:    /Model/Nonlinear models/Logistic

# logit Estimation

Arguments:  variável-dependente variáveis-independentes 
Options:    --robust (robust standard errors)
            --vcv (mostrar matriz de covariância)
            --verbose (mostrar detalhes das iterações)

If the dependent variable is a binary variable (all values are 0 or 1)
maximum likelihood estimates of the coefficients on variáveis-independentes
are obtained via the "binary response model regression" (BRMR) method
outlined by Davidson and MacKinnon (2004). As the model is nonlinear the
slopes depend on the values of the independent variables: the reported
slopes are evaluated at the means of those variables. The chi-square
statistic tests the null hypothesis that all coefficients are zero apart
from the constant.

By default, standard errors are computed using the negative inverse of the
Hessian. If the --robust flag is given, then QML or Huber-White standard
errors are calculated instead. In this case the estimated covariance matrix
is a "sandwich" of the inverse of the estimated Hessian and the outer
product of the gradient. See Davidson and MacKinnon (2004, Chapter 10) for
details.

If the dependent variable is not binary, but is discrete and has a minimum
value of 0, then Ordered Logit estimates are obtained. In this case robust
standard errors are not yet available. (If the variable selected as
dependent is not discrete, or does not have a minimum of zero, an error is
flagged.)

If you want to use logit for analysis of proportions (where the dependent
variable is the proportion of cases having a certain characteristic, at each
observation, rather than a 1 or 0 variable indicating whether the
characteristic is present or not) you should not use the "logit" command,
but rather construct the logit variable, as in

	genr lgt_p = log(p/(1 - p))

and use this as the dependent variable in an OLS regression. See Ramanathan
(2002, Chapter 12).

Menu path:    /Model/Nonlinear models/Logit

# logs Transformations

Argument:   lista-de-variáveis 

The natural log of each of the variables in lista-de-variáveis is obtained
and the result stored in a new variable with the prefix l_ ("el"
underscore). For example, "logs x y" creates the new variables l_x = ln(x)
and l_y = ln(y).

Menu path:    /Add/Logs of selected variables

# loop Programming

Argument:   control 
Options:    --progressive (enable special forms of certain commands)
            --verbose (report details of genr commands)
            --quiet (do not report number of iterations performed)
Examples:   loop 1000
            loop 1000 --progressive
            loop while essdiff > .00001
            loop for i=1991..2000
            loop for (r=-.99; r<=.99; r+=.01)

The parameter "control" must take one of four forms, as shown in the
examples: an integer number of times to repeat the commands within the loop;
"while" plus a numerical condition; "for" plus a range of values for the
internal index variable i; or "for" plus three expressions in parentheses,
separated by semicolons. In the last form the left-hand expression
initializes a variable, the middle expression sets a condition for iteration
to continue, and the right-hand expression sets an increment or decrement to
be applied at the start of the second and subsequent iterations. (This is a
restricted form of the for statement in the C programming language.)

This command opens a special mode in which the program accepts commands to
be executed repeatedly. You exit the mode of entering loop commands with
"endloop": at this point the stacked commands are executed.

See the Gretl User's Guide for further details and examples. The effect of
the --progressive option (which is designed for use in Monte Carlo
simulations) is explained there. Not all gretl commands may be used within a
loop; the commands available in this context are also set out there.

# mahal Statistics

Argument:   lista-de-variáveis 
Options:    --save (add distances to the dataset)
            --vcv (mostrar matriz de covariância)

The Mahalanobis distance is the distance between two points in an
k-dimensional space, scaled by the statistical variation in each dimension
of the space. For example, if p and q are two observations on a set of k
variables with covariance matrix C, then the Mahalanobis distance between
the observations is given by

  sqrt((p - q)' * C-inverse * (p - q))

where (p - q) is a k-vector. This reduces to Euclidean distance if the
covariance matrix is the identity matrix.

The space for which distances are computed is defined by the selected
variables. For each observation in the current sample range, the distance is
computed between the observation and the centroid of the selected variables.
This distance is the multidimensional counterpart of a standard z-score, and
can be used to judge whether a given observation "belongs" with a group of
other observations.

If the --vcv option is given, the covariance matrix and its inverse are
printed. If the --save option is given, the distances are saved to the
dataset under the name mdist (or mdist1, mdist2 and so on if there is
already a variable of that name).

Menu path:    /View/Mahalanobis distances

# meantest Tests

Arguments:  var1 var2 
Option:     --unequal-vars (assume variances are unequal)

Calculates the t statistic for the null hypothesis that the population means
are equal for the variables var1 and var2, and shows its p-value.

By default the test statistic is calculated on the assumption that the
variances are equal for the two variables; with the --unequal-vars option
the variances are assumed to be different. This will make a difference to
the test statistic only if there are different numbers of non-missing
observations for the two variables.

Menu path:    /Model/Bivariate tests/Difference of means

# mle Estimation

Arguments:  log-likelihood function derivatives 
Options:    --vcv (mostrar matriz de covariância)
            --hessian (base covariance matrix on the Hessian)
            --robust (QML covariance matrix)
            --verbose (mostrar detalhes das iterações)
Example:    weibull.inp

Performs Maximum Likelihood (ML) estimation using the BFGS (Broyden,
Fletcher, Goldfarb, Shanno) algorithm. The user must specify the
log-likelihood function. The parameters of this function must be declared
and given starting values (using the "genr" command) prior to estimation.
Optionally, the user may specify the derivatives of the log-likelihood
function with respect to each of the parameters; if analytical derivatives
are not supplied, a numerical approximation is computed.

Simple example: Suppose we have a series X with values 0 or 1 and we wish to
obtain the maximum likelihood estimate of the probability, p, that X = 1.
(In this simple case we can guess in advance that the ML estimate of p will
simply equal the proportion of Xs equal to 1 in the sample.)

The parameter p must first be added to the dataset and given an initial
value. This can be done using the genr command. For example, genr p = 0.5.

We then construct the MLE command block:

	mle loglik = X*log(p) + (1-X)*log(1-p)
	  deriv p = X/p - (1-X)/(1-p)
	end mle

The first line above specifies the log-likelihood function. It starts with
the keyword mle, then a dependent variable is specified and an expression
for the log-likelihood is given (using the same syntax as in the "genr"
command). The next line (which is optional) starts with the keyword deriv
and supplies the derivative of the log-likelihood function with respect to
the parameter p. If no derivatives are given, you should include a statement
using the keyword params which identifies the free parameters: these are
listed on one line, separated by spaces. For example, the above could be
changed to:

	mle loglik = X*log(p) + (1-X)*log(1-p)
	  params p
	end mle

in which case numerical derivatives would be used.

Note that any option flags should be appended to the ending line of the MLE
block.

By default, estimated standard errors are based on the Outer Product of the
Gradient. If the --hessian option is given, they are instead based on the
negative inverse of the Hessian (which is approximated numerically). If the
--robust option is given, a QML estimator is used (namely, a sandwich of the
negative inverse of the Hessian and the covariance matrix of the gradient).

Menu path:    /Model/Maximum likelihood

# modeltab Utilities

Arguments:  add  or show  or free 

Manipulates the gretl "model table". See the Gretl User's Guide for details.
The sub-commands have the following effects: "add" adds the last model
estimated to the model table, if possible; "show" displays the model table
in a window; and "free" clears the table.

Menu path:    Session window, Model table icon

# mpols Estimation

Arguments:  variável-dependente variáveis-independentes 
Options:    --vcv (mostrar matriz de covariância)
            --simple-print (do not print auxiliary statistics)
            --quiet (não mostrar resultados da regressão)

Computes OLS estimates for the specified model using multiple precision
floating-point arithmetic. This command is available only if gretl is
compiled with support for the Gnu Multiple Precision (GMP) library. By
default 256 bits of precision are used for the calculations, but this can be
increased via the environment variable GRETL_MP_BITS. For example, when
using the bash shell one could issue the following command, before starting
gretl, to set a precision of 1024 bits.

	export GRETL_MP_BITS=1024

A rather arcane option is available for this command (primarily for testing
purposes): if the variáveis-independentes list is followed by a semicolon
and a further list of numbers, those numbers are taken as powers of x to be
added to the regression, where x is the last variable in
variáveis-independentes. These additional terms are computed and stored in
multiple precision. In the following example y is regressed on x and the
second, third and fourth powers of x:

	mpols y 0 x ; 2 3 4

Menu path:    /Model/Other linear models/High precision OLS

# nls Estimation

Arguments:  function [ derivatives ] 
Options:    --robust (robust standard errors)
            --vcv (mostrar matriz de covariância)
            --verbose (mostrar detalhes das iterações)
Example:    wg_nls.inp

Performs Nonlinear Least Squares (NLS) estimation using a modified version
of the Levenberg-Marquardt algorithm. You must supply a function
specification. The parameters of this function must be declared and given
starting values (using the "genr" command) prior to estimation. Optionally,
you may specify the derivatives of the regression function with respect to
each of the parameters. If you do not supply derivatives you should instead
give a list of the parameters to be estimated (separated by spaces or
commas), preceded by the keyword params. In the latter case a numerical
approximation to the Jacobian is computed.

It is easiest to show what is required by example. The following is a
complete script to estimate the nonlinear consumption function set out in
William Greene's Econometric Analysis (Chapter 11 of the 4th edition, or
Chapter 9 of the 5th). The numbers to the left of the lines are for
reference and are not part of the commands. Note that any option flags, such
as --vcv for printing the covariance matrix of the parameter estimates,
should be appended to the final command, end nls.

	1   open greene11_3.gdt
	2   ols C 0 Y
	3   genr a = $coeff(0)
	4   genr b = $coeff(Y)
	5   genr g = 1.0
	6   nls C = a + b * Y^g
	7    deriv a = 1
	8    deriv b = Y^g
	9    deriv g = b * Y^g * log(Y)
	10  end nls --vcv

It is often convenient to initialize the parameters by reference to a
related linear model; that is accomplished here on lines 2 to 5. The
parameters alpha, beta and gamma could be set to any initial values (not
necessarily based on a model estimated with OLS), although convergence of
the NLS procedure is not guaranteed for an arbitrary starting point.

The actual NLS commands occupy lines 6 to 10. On line 6 the "nls" command is
given: a dependent variable is specified, followed by an equals sign,
followed by a function specification. The syntax for the expression on the
right is the same as that for the "genr" command. The next three lines
specify the derivatives of the regression function with respect to each of
the parameters in turn. Each line begins with the keyword "deriv", gives the
name of a parameter, an equals sign, and an expression whereby the
derivative can be calculated (again, the syntax here is the same as for
"genr"). As an alternative to supplying numerical derivatives, you could
substitute the following for lines 7 to 9:

	params a b g

Line 10, "end nls", completes the command and calls for estimation.

For further details on NLS estimation please see the Gretl User's Guide.

Menu path:    /Model/Nonlinear models/Nonlinear Least Squares

# normtest Tests

Argument:   series 
Options:    --dhansen (Doornik-Hansen test)
            --swilk (Shapiro-Wilk test)
            --lillie (Lilliefors test)
            --jbera (Jarque-Bera test)
            --all (do all tests)
            --quiet (suppress printed output)

Carries out a test for normality for the given series. The specific test is
controlled by the option flags (but if no flag is given, the Doornik-Hansen
test is performed). Note: the Doornik-Hansen and Shapiro-Wilk tests are
recommended over the others, on account of their superior small-sample
properties.

The test statistic and its p-value may be retrieved using the accessors
$test and $pvalue. Please note that if the --all option is given, the result
recorded is that from the Doornik-Hansen test.

Menu path:    /Variable/Normality test

# nulldata Dataset

Argument:   series_length 
Example:    nulldata 500

Establishes a "blank" data set, containing only a constant and an index
variable, with periodicity 1 and the specified number of observations. This
may be used for simulation purposes: some of the "genr" commands (e.g. "genr
uniform()", "genr normal()") will generate dummy data from scratch to fill
out the data set. This command may be useful in conjunction with "loop". See
also the "seed" option to the "set" command.

Menu path:    /Ficheiro/New data set

# ols Estimation

Arguments:  variável-dependente variáveis-independentes 
Options:    --vcv (mostrar matriz de covariância)
            --robust (robust standard errors)
            --simple-print (do not print auxiliary statistics)
            --quiet (não mostrar resultados da regressão)
            --no-df-corr (suppress degrees of freedom correction)
            --print-final (see below)
Examples:   ols 1 0 2 4 6 7
            ols y 0 x1 x2 x3 --vcv
            ols y 0 x1 x2 x3 --quiet

Computes ordinary least squares (OLS) estimates with depvar as the dependent
variable and variáveis-independentes as the list of independent variables.
Variables may be specified by name or number; use the number zero for a
constant term.

Besides coefficient estimates and standard errors, the program also prints
p-values for t (two-tailed) and F-statistics. A p-value below 0.01 indicates
statistical significance at the 1 percent level and is marked with ***. **
indicates significance between 1 and 5 percent and * indicates significance
between the 5 and 10 percent levels. Model selection statistics (the Akaike
Information Criterion or AIC and Schwarz's Bayesian Information Criterion)
are also printed. The formula used for the AIC is that given by Akaike
(1974), namely minus two times the maximized log-likelihood plus two times
the number of parameters estimated.

If the option --no-df-corr is given, the usual degrees of freedom correction
is not applied when calculating the estimated error variance (and hence also
the standard errors of the parameter estimates).

The option --print-final is applicable only in the context of a "loop". It
arranges for the regression to be run silently on all but the final
iteration of the loop. See the Gretl User's Guide for details.

Various internal variables may be retrieved using the "genr" command,
provided "genr" is invoked immediately after this command.

The specific formula used for generating robust standard errors (when the
--robust option is given) can be adjusted via the "set" command.

Menu path:    /Model/Ordinary Least Squares
Other access: Beta-hat button on toolbar

# omit Tests

Argument:   lista-de-variáveis 
Options:    --wald (do a Wald test rather than an F-test)
            --vcv (mostrar matriz de covariância)
            --quiet (don't print estimates for reduced model)
            --silent (don't print anything)
            --inst (omit as instrument, TSLS only)
            --both (omit as both regressor and instrument, TSLS only)
Examples:   omit 5 7 9
            omit seasonals --quiet

This command must follow an estimation command. It calculates a test
statistic for the joint significance of the variables in
lista-de-variáveis, which should be a subset of the independent variables
in the model previously estimated.

If the original model was estimated by OLS, the test statistic is by default
an F-value. This is based on the sums of squared residuals for the
restricted and unrestricted models, unless the original model was estimated
with robust standard errors. In the latter case F is computed from the
robust estimate of the covariance matrix for the original model. (It is the
F-form of a Wald test).

For estimators other than OLS, or if the --wald option is given, the
statistic is an asymptotic Wald chi-square value based on the covariance
matrix of the original model.

By default, the restricted model is estimated, the estimates are printed,
and the restricted model replaces the original as the "current model" for
the purposes of, for example, retrieving the residuals as $uhat (or doing
further tests such as "add" or "omit").

If the --wald option is selected, the restricted model is not estimated (and
so the current model is not replaced). The --quiet option suppresses the
printout of the restricted model (if applicable): only the result of the
test is printed. If the restricted model is both estimated and printed, the
--vcv option has the effect of printing the covariance matrix for the
coefficients in the restricted model, otherwise this option is ignored.

If the --silent option is given, nothing is printed; nonetheless, the
results of the test can be retrieved using the special variables $test and
$pvalue.

If the original model was estimated using two-stage least squares, an
ambiguity arises: should the selected variables be omitted as regressors, as
instruments, or as both? This is resolved as follows: by default the
variables are dropped from the list of regressors, but if the --inst flag is
given they are dropped as instruments, or if the --both flag is given they
are dropped from the model altogether. These two options are incompatible
with the --wald option; if one or more instruments are omitted the model
must be re-estimated.

Menu path:    Janela do modelo, /Testes/Omit variables

# open Dataset

Argument:   ficheiro-de-dados 
Option:     --www (use a database on the gretl server)
Examples:   open data4-1
            open voter.dta
            open fedbog --www

Opens a data file. If a data file is already open, it is replaced by the
newly opened one. If a full path is not given, the program will search some
relevant paths to try to find the file. If no filename suffix is given (as
in the first example above), gretl assumes a native datafile with suffix
.gdt. Based on the name of the file and various heuristics, gretl will try
to detect the format of the data file (native, plain text, CSV, MS Excel,
Stata, etc.).

This command can also be used to open a database (gretl or RATS 4.0) for
reading. In that case it should be followed by the "data" command to extract
particular series from the database. If the www option is given, the program
will try to access a database of the given name on the gretl server -- for
instance the Federal Reserve interest rates database in the third example
above.

Menu path:    /Ficheiro/Open data
Other access: Drag a data file into gretl (MS Windows or Gnome)

# orthdev Transformations

Argument:   varlist 

Applicable with panel data only. A series of forward orthogonal deviations
is obtained for each variable in varlist and stored in a new variable with
the prefix o_. Thus "orthdev x y" creates the new variables o_x and o_y.

The values are stored one step ahead of their true temporal location (that
is, o_x at observation t holds the deviation that, strictly speaking,
belongs at t - 1). This is for compatibility with first differences: one
loses the first observation in each time series, not the last.

# outfile Printing

Arguments:  filename option 
Options:    --append (append to file)
            --close (close file)
            --write (overwrite file)
Examples:   outfile --write regress.txt
            outfile --close

Diverts output to filename, until further notice. Use the flag --append to
append output to an existing file or --write to start a new file (or
overwrite an existing one). Only one file can be opened in this way at any
given time.

The --close flag is used to close an output file that was previously opened
as above. Output will then revert to the default stream.

In the first example command above, the file regress.txt is opened for
writing, and in the second it is closed. This would make sense as a sequence
only if some commands were issued before the --close. For example if an
"ols" command intervened, its output would go to regress.txt rather than the
screen.

# panel Estimation

Options:    --vcv (mostrar matriz de covariância)
            --random-effects (random rather than fixed effects)
            --between (estimate the between-groups model)
            --time-dummies (include time dummy variables)
            --unit-weights (weighted least squares)
            --iterate (iterative estimation)
            --quiet (less verbose output)
            --verbose (more verbose output)

Estimates a panel model, by default using the fixed effects estimator.
Depending on the number of cross-sectional units and the number of
independent variables, this is implemented either by adding a set of dummy
variables representing the units, or by subtracting the group or unit means
from the original data.

If the --random-effects flag is given, random effects estimates are
computed, using the method of Swamy and Arora.

Alternatively, if the --unit-weights flag is given, the model is estimated
via weighted least squares, with the weights based on the residual variance
for the respective cross-sectional units in the sample. In this case (only)
the --iterate flag may be added to produce iterative estimates: if the
iteration converges, the resulting estimates are Maximum Likelihood.

As a further alternative, if the --between flag is given, the between-groups
model is estimated (that is, an OLS regression using the group means).

For more details on panel estimation, please see the Gretl User's Guide.

Menu path:    /Model/Panel

# pca Statistics

Argument:   lista-de-variáveis 
Options:    --save (Save major components)
            --save-all (Save all components)

Principal Components Analysis. Prints the eigenvalues of the correlation
matrix for the variables in lista-de-variáveis along with the proportion of
the joint variance accounted for by each component. Also prints the
corresponding eigenvectors (or "component loadings").

If the --save flag is given, components with eigenvalues greater than 1.0
are saved to the dataset as variables, with names PC1, PC2 and so on. These
artificial variables are formed as the sum of (component loading) times
(standardized Xi), where Xi denotes the ith variable in lista-de-variáveis.

If the --save-all flag is given, all of the components are saved as
described above.

Menu path:    /View/Principal components
Other access: Main window pop-up (multiple selection)

# pergm Statistics

Arguments:  nome-de-variável [ bandwidth ] 
Options:    --bartlett (use Bartlett lag window)
            --log (use log scale)

Computes and displays (and if not in batch mode, graphs) the spectrum of the
specified variable. By default the sample periodogram is given; with the
--bartlett flag a Bartlett lag window is used in estimating the spectrum
(see, for example, Greene's Econometric Analysis for a discussion of this).
The default width of the Bartlett window is twice the square root of the
sample size but this can be set manually using the bandwidth parameter, up
to a maximum of half the sample size. If the --log option is given the
spectrum is represented on a logarithmic scale.

When the sample periodogram is printed, two tests for fractional integration
of the series ("long memory") are given, namely the Geweke and Porter-Hudak
(GPH) test and the Local Whittle Estimator. The null hypothesis in both
cases is that the integration order is zero. By default the order for these
tests is the lesser of T/2 and T0.6. Again, this value can be adjusted using
the bandwidth parameter.

Menu path:    /Variável/Spectrum
Other access: Menu de contexto da janela principal (selecção singular)

# poisson Estimation

Arguments:  variável-dependente variáveis-independentes [ ; offset ] 
Options:    --vcv (mostrar matriz de covariância)
            --verbose (mostrar detalhes das iterações)
Examples:   poisson y 0 x1 x2
            poisson y 0 x1 x2 ; S

Estimates a poisson regression. The dependent variable is taken to represent
the occurrence of events of some sort, and must take on only non-negative
integer values.

If a discrete random variable Y follows the Poisson distribution, then

  Pr(Y = y) = exp(-v) * v^y / y!

for y = 0, 1, 2,.... The mean and variance of the distribution are both
equal to v. In the Poisson regression model, the parameter v is represented
as a function of one or more independent variables. The most common version
(and the only one supported by gretl) has

  v = exp(b0 + b1*x1 + b2*x2 + ...)

or in other words the log of v is a linear function of the independent
variables.

Optionally, you may add an "offset" variable to the specification. This is a
scale variable, the log of which is added to the linear regression function
(implicitly, with a coefficient of 1.0). This makes sense if you expect the
number of occurrences of the event in question to be proportional, other
things equal, to some known factor. For example, the number of traffic
accidents might be supposed to be proportional to traffic volume, other
things equal, and in that case traffic volume could be specified as an
"offset" in a Poisson model of the accident rate. The offset variable must
be strictly positive.

Menu path:    /Model/Nonlinear models/Poisson

# plot Graphs

Argument:   lista-de-variáveis 
Option:     --one-scale (force a single scale)

Plots the values for specified variables, for the range of observations
currently in effect, using ASCII symbols. Each line stands for an
observation and the values are plotted horizontally. By default the
variables are scaled appropriately. See also "gnuplot".

# print Printing

Arguments:  lista-de-variáveis  or string_literal 
Options:    --byobs (by observations)
            --long (use 10 significant digits or more)
            --no-dates (use simple observation numbers)
Examples:   print x1 x2 --byobs
            print "This is a string"

If lista-de-variáveis is given, prints the values of the specified
variables; if no list is given, prints the values of all variables in the
current data file. If the --byobs flag is given the data are printed by
observation, otherwise they are printed by variable.

If the --long flag is given the data are printed, by variable, to greater
than usual precision. The default in this case is to show 10 significant
digits but you can adjust that figure using the "set" command.

If the --byobs flag is given and the data are printed by observation, the
default is to show the date (with time-series data) or the observation
marker string (if any) at the start of each line. The --no-dates option
suppresses the printing of dates or markers; a simple observation number is
shown instead.

If the argument to "print" is a literal string (which must start with a
double-quote, "), the string is printed as is. See also "printf".

Note: a special "hack" is available with this command, in conjunction with
the --byobs flag, which can be useful when working with missing values in a
data set. If you give a list of variables followed by a semi-colon, followed
by one final variable, then the final variable is not printed but is used to
screen the observations to print. Any observations for which the screening
variable has value 0 are not printed. As an example of use, suppose you have
a daily time series x, and you want a list of the dates for which x is
missing. You can do

	genr filt = missing(x)
	print x ; filt --byobs

Menu path:    /Data/Display values

# printf Printing

Arguments:  format args 

Prints scalar values and/or strings under the control of a format string
(providing a small subset of the printf() statement in the C programming
language). Recognized numeric formats are %e, %E, %f, %g, %G and %d, in each
case with the various modifiers available in C. Examples: the format %.10g
prints a value to 10 significant figures; %12.6f prints a value to 6 decimal
places, with a width of 12 characters. The format %s should be used for
strings.

The format string itself must be enclosed in double quotes. The values to be
printed must follow the format string, separated by commas. These values
should take the form of either (a) the names of variables in the dataset,
(b) expressions that are valid for the "genr" command, or (c) the special
functions varname() or date(). The following example prints the values of
two variables plus that of a calculated expression:

	ols 1 0 2 3
	genr b = $coeff(2)
	genr se_b = $stderr(2)
	printf "b = %.8g, standard error %.8g, t = %.4f\n", b, se_b, b/se_b

The next lines illustrate the use of the varname and date functions, which
respectively print the name of a variable, given its ID number, and a date
string, given a 1-based observation number.

	printf "The name of variable %d is %s\n", i, varname(i)
	printf "The date of observation %d is %s\n", j, date(j)

The maximum length of a format string is 127 characters. The escape
sequences \n (newline), \t (tab), \v (vertical tab) and \\ (literal
backslash) are recognized. To print a literal percent sign, use %%.

# probit Estimation

Arguments:  variável-dependente variáveis-independentes 
Options:    --robust (robust standard errors)
            --vcv (mostrar matriz de covariância)
            --verbose (mostrar detalhes das iterações)

If the dependent variable is a binary variable (all values are 0 or 1)
maximum likelihood estimates of the coefficients on variáveis-independentes
are obtained via the "binary response model regression" (BRMR) method
outlined by Davidson and MacKinnon (2004). As the model is nonlinear the
slopes depend on the values of the independent variables: the reported
slopes are evaluated at the means of those variables. The chi-square
statistic tests the null hypothesis that all coefficients are zero apart
from the constant.

By default, standard errors are computed using the negative inverse of the
Hessian. If the --robust flag is given, then QML or Huber-White standard
errors are calculated instead. In this case the estimated covariance matrix
is a "sandwich" of the inverse of the estimated Hessian and the outer
product of the gradient. See Davidson and MacKinnon (2004, Chapter 10) for
details.

If the dependent variable is not binary, but is discrete and has a minimum
value of 0, then Ordered Probit estimates are obtained. In this case robust
standard errors are not yet available. (If the variable selected as
dependent is not discrete, or does not have a minimum of zero, an error is
flagged.)

Probit for analysis of proportions is not implemented in gretl at this
point.

Menu path:    /Model/Nonlinear models/Probit

# pvalue Utilities

Arguments:  dist [ params ] xval 
Examples:   pvalue z zscore
            pvalue t 25 3.0
            pvalue X 3 5.6
            pvalue F 4 58 fval
            pvalue G xbar varx x
            pvalue B bprob 10 6

Computes the area to the right of xval in the specified distribution (z for
Gaussian, t for Student's t, X for chi-square, F for F, G for gamma, or B
for binomial).

For the t and chi-square distributions the degrees of freedom must be given;
for F numerator and denominator degrees of freedom are required; for gamma
the mean and variance are needed; and for the binomial distribution the
"success" probability and the number of trials must be given. In each case,
these extra values are provided before the xval.

As shown in the examples above, the numerical parameters may be given in
numeric form or as the names of variables.

Menu path:    /Tools/P-value finder

# qlrtest Tests

For a model estimated on time-series data via OLS, performs the Quandt
likelihood ratio (QLR) test for a structural break at an unknown point in
time, with 15 percent trimming at the beginning and end of the sample
period.

For each potential break point within the central 70 percent of the
observations, a Chow test is performed (see "chow"). The QLR test statistic
is the maximum of the F values from these tests. It follows a non-standard
distribution, the critical values of which are taken from Stock and Watson's
Introduction to Econometrics (2003). If the QLR statistic exceeds the
critical value at the chosen level of significance, one can infer that the
parameters of the model are not constant. This statistic can be used to
detect forms of instability other than a single discrete break (such as
multiple breaks or a slow drifting of the parameters).

Menu path:    Janela do modelo, /Testes/Teste QLR test

# quantreg Estimation

Arguments:  tau depvar indepvars 
Options:    --robust (robust standard errors)
            --intervals=level (compute confidence intervals)
            --vcv (print covariance matrix)
Examples:   quantreg 0.25 y 0 xlist
            quantreg 0.5 y 0 xlist --intervals
            quantreg 0.5 y 0 xlist --intervals=.95
            quantreg tauvec y 0 xlist --robust
            See also mrw_qr.inp

Quantile regression. The first argument, tau, is the conditional quantile
for which estimates are wanted. It may be given either as a numerical value
or as the name of a pre-defined scalar variable; the value must be in the
range 0.01 to 0.99. (Alternatively, a vector of values may be given for tau;
see below for details.) The second and subsequent arguments compose a
regression list on the same pattern as "ols".

Without the --intervals option, standard errors are printed for the quantile
estimates. By default, these are computed according to the asymptotic
formula given by Koenker and Bassett (1978), but if the --robust option is
given, standard errors that are robust with respect to heteroskedasticity
are calculated using the method of Koenker and Zhao (1994).

When the --intervals option is chosen, confidence intervals are given for
the parameter estimates instead of standard errors. These intervals are
computed using the rank inversion method, and in general they are
asymmetrical about the point estimates. The specifics of the calculation are
inflected by the --robust option: without this, the intervals are computed
on the assumption of IID errors (Koenker, 1994); with it, they use the
robust estimator developed by Koenker and Machado (1999).

By default, 90 percent confidence intervals are produced. You can change
this by appending a confidence level (expressed as a decimal fraction) to
the intervals option, as in --intervals=0.95.

Vector-valued tau: instead of supplying a scalar, you may give the name of a
pre-defined matrix. In this case estimates are computed for all the given
tau values and the results are printed in a special format, showing the
sequence of quantile estimates for each regressor in turn.

Menu path:    /Model/Robust estimation/Quantile regression

# quit Utilities

Exits from the program, giving you the option of saving the output from the
session on the way out.

Menu path:    /Ficheiro/Exit

# rename Dataset

Variants:   rename varnumber newname
            rename varname newname

Changes the name of the variable with identification number varnumber or
current name varname to newname. The new name must be of 15 characters
maximum, must start with a letter, and must be composed of only letters,
digits, and the underscore character.

Menu path:    /Variável/Edit attributes
Other access: Menu de contexto da janela principal (selecção singular)

# reset Tests

Must follow the estimation of a model via OLS. Carries out Ramsey's RESET
test for model specification (non-linearity) by adding the square and the
cube of the fitted values to the regression and calculating the F statistic
for the null hypothesis that the parameters on the two added terms are zero.

Menu path:    Janela do modelo, /Testes/Ramsey's RESET

# restrict Tests

Imposes a set of linear restrictions on either (a) the model last estimated
or (b) a system of equations previously defined and named. The syntax and
effects of the command differ slightly in the two cases.

In both cases the set of restrictions should be started with the keyword
"restrict" and terminated with "end restrict". In the single equation case
the restrictions are implicitly to be applied to the last model, and they
are evaluated as soon as the restrict command is terminated. In the system
case the initial "restrict" must be followed by the name of a previously
defined system of equations (see "system"). The restrictions are evaluated
when the system is next estimated, using the "estimate" command.

Each restriction in the set should be expressed as an equation, with a
linear combination of parameters on the left and a numeric value to the
right of the equals sign. In the single-equation case, parameters may be
referenced in the form b[i], where i represents the position in the list of
regressors (starting at 1), or b[varname], where varname is the name of the
regressor in question. In the system case, parameters are referenced using b
plus two numbers in square brackets. The leading number represents the
position of the equation within the system and the second number indicates
position in the list of regressors. For example b[2,1] denotes the first
parameter in the second equation, and b[3,2] the second parameter in the
third equation.

The b terms in the equation representing a restriction equation may be
prefixed with a numeric multiplier, using * to represent multiplication, for
example 3.5*b[4].

Here is an example of a set of restrictions for a previously estimated
model:

	restrict
	 b[1] = 0
	 b[2] - b[3] = 0
	 b[4] + 2*b[5] = 1
	end restrict

And here is an example of a set of restrictions to be applied to a named
system. (If the name of the system does not contain spaces, the surrounding
quotes are not required.)

	restrict "System 1"
	 b[1,1] = 0
	 b[1,2] - b[2,2] = 0
	 b[3,4] + 2*b[3,5] = 1
	end restrict

In the single-equation case the restrictions are evaluated via a Wald
F-test, using the coefficient covariance matrix of the model in question. By
default, the restricted coefficient estimates are printed; if you just want
the test statistic, you can append the --quiet option flag to the initial
restrict command.

In the system case, the test statistic depends on the estimator chosen: a
Likelihood Ratio test if the system is estimated using a Maximum Likelihood
method, or an asymptotic F-test otherwise.

Menu path:    Janela do modelo, /Testes/Linear restrictions

# rhodiff Transformations

Arguments:  rholist ; lista-de-variáveis 
Examples:   rhodiff .65 ; 2 3 4
            rhodiff r1 r2 ; x1 x2 x3

Creates rho-differenced counterparts of the variables (given by number or by
name) in lista-de-variáveis and adds them to the data set, using the suffix
# for the new variables. Given variable v1 in lista-de-variáveis, and
entries r1 and r2 in rholist, the new variable

	v1# = v1 - r1*v1(-1) - r2*v1(-2)

is created. The rholist entries can be given as numerical values or as the
names of variables previously defined.

# rmplot Graphs

Argument:   nome-de-variável 

Range-mean plot: this command creates a simple graph to help in deciding
whether a time series, y(t), has constant variance or not. We take the full
sample t=1,...,T and divide it into small subsamples of arbitrary size k.
The first subsample is formed by y(1),...,y(k), the second is y(k+1), ...,
y(2k), and so on. For each subsample we calculate the sample mean and range
(= maximum minus minimum), and we construct a graph with the means on the
horizontal axis and the ranges on the vertical. So each subsample is
represented by a point in this plane. If the variance of the series is
constant we would expect the subsample range to be independent of the
subsample mean; if we see the points approximate an upward-sloping line this
suggests the variance of the series is increasing in its mean; and if the
points approximate a downward sloping line this suggests the variance is
decreasing in the mean.

Besides the graph, gretl displays the means and ranges for each subsample,
along with the slope coefficient for an OLS regression of the range on the
mean and the p-value for the null hypothesis that this slope is zero. If the
slope coefficient is significant at the 10 percent significance level then
the fitted line from the regression of range on mean is shown on the graph.

Menu path:    /Variável/Range-mean graph

# run Programming

Argument:   inputfile 

Execute the commands in inputfile then return control to the interactive
prompt. This command is intended for use with the command-line program
gretlcli, or at the "gretl console" in the GUI program.

See also "include".

Menu path:    Run icon in script window

# runs Tests

Argument:   nome-de-variável 

Carries out the nonparametric "runs" test for randomness of the specified
variable. If you want to test for randomness of deviations from the median,
for a variable named x1 with a non-zero median, you can do the following:

	genr signx1 = x1 - median(x1)
	runs signx1

Menu path:    /Variável/Runs test

# scatters Graphs

Arguments:  yvar ; xvarlist  or yvarlist ; xvar 
Option:     --with-lines (create line graphs)
Examples:   scatters 1 ; 2 3 4 5
            scatters 1 2 3 4 5 6 ; 7
            scatters y1 y2 y3 ; x --with-lines

Generates pairwise graphs of yvar against all the variables in xvarlist, or
of all the variables in yvarlist against xvar. The first example above puts
variable 1 on the y-axis and draws four graphs, the first having variable 2
on the x-axis, the second variable 3 on the x-axis, and so on. The second
example plots each of variables 1 through 6 against variable 7 on the
x-axis. Scanning a set of such plots can be a useful step in exploratory
data analysis. The maximum number of plots is six; any extra variable in the
list will be ignored.

By default the graphs are scatterplots, but if you give the --with-lines
flag they will be line graphs.

Menu path:    /View/Multiple graphs

# sdiff Transformations

Argument:   lista-de-variáveis 

The seasonal difference of each variable in lista-de-variáveis is obtained
and the result stored in a new variable with the prefix sd_. This command is
available only for seasonal time series.

Menu path:    /Add/Seasonal differences of selected variables

# set Programming

Arguments:  variável value 
Examples:   set qr on
            set csv_delim tab
            set horizon 10

Set the values of various program parameters. The given value remains in
force for the duration of the gretl session unless it is changed by a
further call to "set". The parameters that can be set in this way are
enumerated below. Note that the settings of hac_lag and hc_version are used
when the --robust option is given to the "ols" command.

If the "set" command is given without any parameters, the current settings
for all the relevant variables are printed.

  echo: off or on (the default). Suppress or resume the echoing of commands
  in gretl's output.

  messages: off or on (the default). Suppress or resume the printing of
  non-error messages associated with various commands, for example when a
  new variable is generated or when the sample range is changed.

  nls_toler: a floating-point value (the default is the machine precision to
  the power 3/4). Sets the tolerance used in judging whether or not
  convergence has occurred in nonlinear least squares estimation using the
  "nls" command.

  qr: on or off (the default). Use QR rather than Cholesky decomposition in
  calculating OLS estimates.

  seed: an unsigned integer. Sets the seed for the pseudo-random number
  generator. By default this is set from the system time; if you want to
  generate repeatable sequences of random numbers you must set the seed
  manually.

  hac_lag: nw1 (the default) or nw2, or an integer. Sets the maximum lag
  value, p, used when calculating HAC (Heteroskedasticity and
  Autocorrelation Consistent) standard errors using the Newey-West approach,
  for time series data. nw1 and nw2 represent two variant automatic
  calculations based on the sample size, T: for nw1, p = 0.75 * T^(1/3), and
  for nw2, p = 4 * (T/100)^(2/9).

  hc_version: 0 (the default), 1, 2 or 3. Sets the variant used when
  calculating Heteroskedasticity Consistent standard errors with
  cross-sectional data. The options correspond to the HC0, HC1, HC2 and HC3
  discussed by Davidson and MacKinnon in Econometric Theory and Methods,
  chapter 5. HC0 produces what are usually called "White's standard errors".

  force_hc: off (the default) or on. By default, with time-series data and
  when the --robust option is given with ols, the HAC estimator is used. If
  you set force_hc to "on", this forces calculation of the regular
  Heteroskedasticity Consistent Covariance Matrix (which does not take
  autocorrelation into account).

  garch_vcv: unset, hessian, im (information matrix) , op (outer product
  matrix), qml (QML estimator), bw (Bollerslev-Wooldridge). Specifies the
  variant that will be used for estimating the coefficient covariance
  matrix, for GARCH models. If unset is given (the default) then the Hessian
  is used unless the "robust" option is given for the garch command, in
  which case QML is used.

  hp_lambda: auto (the default), or a numerical value. Sets the smoothing
  parameter for the Hodrick-Prescott filter (see the hpfilt function under
  the genr command). The default is to use 100 times the square of the
  periodicity, which gives 100 for annual data, 1600 for quarterly data, and
  so on.

  bkbp_limits: two integers, the second greater than the first (the defaults
  are 8 and 32). Sets the frequency bounds for the Baxter-King bandpass
  filter (see the bkfilt function under the genr command).

  bkbp_k: one integer (the default is 8). Sets the approximation order for
  the Baxter-King bandpass filter.

  horizon: one integer (the default is based on the frequency of the data).
  Sets the horizon for impulse responses and forecast variance
  decompositions in the context of vector autoregressions.

  csv_delim: either comma (the default), space or tab. Sets the column
  delimiter used when saving data to file in CSV format.

  bhhh_maxiter: one integer, maximum number of iterations for gretl's
  internal BHHH routine, which is used in the "arma" command for conditional
  ML estimation. If convergence is not achieved after bhhh_maxiter, the
  program returns an error. The default is set at 500.

  bhhh_toler: one floating point value, or the string default. This is used
  in gretl's internal BHHH routine to check if convergence has occurred. The
  algorithm stops iterating as soon as the increment in the log-likelihood
  between iterations is smaller than bhhh_toler. The default value is
  1.0E-06; this value may be re-established by typing default in place of a
  numeric value.

  longdigits: one positive integer value, less than or equal to 20.
  Determines the number of digits of precision used when printing the values
  of variables using the --long option (see "print").

  initvals: a pre-specified matrix. Allows manual setting of the initial
  parameter estimates for ARMA estimation. For details see the Gretl User's
  Guide.

# setinfo Dataset

Arguments:  nome-de-variável -d description -n displayname 
Options:    --discrete (mark variable as discrete)
            --continuous (mark variable as continuous)
Examples:   setinfo x1 -d "Description of x1" -n "Graph name"
            setinfo z --discrete

Sets up to three attributes of the named variable, as follows.

If the -d flag is given followed by a string in double quotes, that string
is used to set the variable's descriptive label. This label is shown in
response to the "labels" command, and is also shown in the main window of
the GUI program.

If the -n flag is given followed by a quoted string, that string is used to
set the variable's "display name", which is then used in place of the
variable's name in graphs.

If one or other of the --discrete or --continuous option flags is given, the
variable's numerical character is set accordingly. The default is to treat
all variables as continuous; setting a variable as discrete affects the way
the variable is handled in frequency plots.

Menu path:    /Variável/Edit attributes
Other access: Menu de contexto da janela principal

# setobs Dataset

Variants:   setobs periodicity startobs
            setobs unitvar timevar
Options:    --cross-section (interpret as cross section)
            --time-series (interpret as time series)
            --stacked-cross-section (interpret as panel data)
            --stacked-time-series (interpret as panel data)
            --panel-vars (use index variables (see below))
Examples:   setobs 4 1990:1 --time-series
            setobs 12 1978:03
            setobs 1 1 --cross-section
            setobs 20 1:1 --stacked-time-series
            setobs unit year --panel-vars

Force the program to interpret the current data set as having a specified
structure.

In the first form of the command the periodicity, which must be an integer,
represents frequency in the case of time-series data (1 = annual; 4 =
quarterly; 12 = monthly; 52 = weekly; 5, 6, or 7 = daily; 24 = hourly). In
the case of panel data the periodicity means the number of lines per data
block: this corresponds to the number of cross-sectional units in the case
of stacked cross-sections, or the number of time periods in the case of
stacked time series. In the case of simple cross-sectional data the
periodicity should be set to 1.

The starting observation represents the starting date in the case of time
series data. Years may be given with two or four digits; subperiods (for
example, quarters or months) should be separated from the year with a colon.
In the case of panel data the starting observation should be given as 1:1;
and in the case of cross-sectional data, as 1. Starting observations for
daily or weekly data should be given in the form YY/MM/DD or YYYY/MM/DD (or
simply as 1 for undated data).

The second form of the command (which requires the --panel-vars flag) may be
used to impose a panel interpretation when the data set contains variables
that uniquely identify the cross-sectional units and the time periods. The
data set will be sorted as stacked time series, by ascending values of the
units variable, unitvar.

If no explicit option flag is given to indicate the structure of the data
the program will attempt to guess the structure from the information given.

Menu path:    /Data/Dataset structure

# setmiss Dataset

Arguments:  value [ lista-de-variáveis ] 
Examples:   setmiss -1
            setmiss 100 x2

Get the program to interpret some specific numerical data value (the first
parameter to the command) as a code for "missing", in the case of imported
data. If this value is the only parameter, as in the first example above,
the interpretation will be applied to all series in the data set. If "value"
is followed by a list of variables, by name or number, the interpretation is
confined to the specified variable(s). Thus in the second example the data
value 100 is interpreted as a code for "missing", but only for the variable
x2.

Menu path:    /Sample/Set missing value code

# shell Utilities

Argument:   shellcommand 
Examples:   ! ls -al
            ! notepad
            launch notepad

A "!", or the keyword "launch", at the beginning of a command line is
interpreted as an escape to the user's shell. Thus arbitrary shell commands
can be executed from within gretl. When "!" is used, the external command is
executed synchronously. That is, gretl waits for it to complete before
proceeding. If you want to start another program from within gretl and not
wait for its completion (asynchronous operation), use "launch" instead.

For reasons of security this facility is not enabled by default. To activate
it, check the box titled "Allow shell commands" under the File, Preferences
menu in the GUI program. This also makes shell commands available in the
command-line program (and is the only way to do so).

# smpl Dataset

Variants:   smpl startobs endobs
            smpl +i -j
            smpl dumvar --dummy
            smpl condition --restrict
            smpl --no-missing [ lista-de-variáveis ]
            smpl n --random
            smpl full
Examples:   smpl 3 10
            smpl 1960:2 1982:4
            smpl +1 -1
            smpl x > 3000 --restrict
            smpl y > 3000 --restrict --replace
            smpl 100 --random

Resets the sample range. The new range can be defined in several ways. In
the first alternate form (and the first two examples) above, startobs and
endobs must be consistent with the periodicity of the data. Either one may
be replaced by a semicolon to leave the value unchanged. In the second form,
the integers i and j (which may be positive or negative, and should be
signed) are taken as offsets relative to the existing sample range. In the
third form dummyvar must be an indicator variable with values 0 or 1 at each
observation; the sample will be restricted to observations where the value
is 1. The fourth form, using --restrict, restricts the sample to
observations that satisfy the given Boolean condition (which is specified
according to the syntax of the "genr" command).

With the --no-missing form, if lista-de-variáveis is specified observations
are selected on condition that all variables in lista-de-variáveis have
valid values at that observation; otherwise, if no lista-de-variáveis is
given, observations are selected on condition that all variables have valid
(non-missing) values.

With the --random flag, the specified number of cases are selected from the
full dataset at random. If you wish to be able to replicate this selection
you should set the seed for the random number generator first (see the "set"
command).

The final form, smpl full, restores the full data range.

Note that sample restrictions are, by default, cumulative: the baseline for
any smpl command is the current sample. If you wish the command to act so as
to replace any existing restriction you can add the option flag --replace to
the end of the command.

The internal variable obs may be used with the --restrict form of smpl to
exclude particular observations from the sample. For example

	smpl obs!=4 --restrict

will drop just the fourth observation. If the data points are identified by
labels,

	smpl obs!="USA" --restrict

will drop the observation with label "USA".

One point should be noted about the --dummy, --restrict and --no-missing
forms of smpl: Any "structural" information in the data file (regarding the
time series or panel nature of the data) is lost when this command is
issued. You may reimpose structure with the "setobs" command.

Please see the Gretl User's Guide for further details.

Menu path:    /Sample

# spearman Statistics

Arguments:  x y 
Option:     --verbose (print ranked data)

Prints Spearman's rank correlation coefficient for the two variables x and
y. The variables do not have to be ranked manually in advance; the function
takes care of this.

The automatic ranking is from largest to smallest (i.e. the largest data
value gets rank 1). If you need to invert this ranking, create a new
variable which is the negative of the original first. For example:

	genr altx = -x
	spearman altx y

Menu path:    /Model/Robust estimation/Rank correlation

# sprintf Printing

Arguments:  stringvar format args 

This command works exactly like the "printf" command, printing the given
arguments under the control of the format string, except that the result is
written into the named string, stringvar.

# square Transformations

Argument:   lista-de-variáveis 
Option:     --cross (generate cross-products as well as squares)

Generates new variables which are squares of the variables in
lista-de-variáveis (plus cross-products if the --cross option is given).
For example, "square x y" will generate sq_x = x squared, sq_y = y squared
and (optionally) x_y = x times y. If a particular variable is a dummy
variable it is not squared because we will get the same variable.

Menu path:    /Add/Squares of selected variables

# sscanf Programming

Arguments:  source , format , args 

Scans the string source under the control of format, assigning zero or more
values to the given args. This is a simplifed version of the sscanf function
in the C programming language.

source may be either a literal string, enclosed in double quotes, or the
name of a predefined string variable. format is defined similarly to the
format string in "printf" (more on this below). args should be a
comma-separated list containing the names of pre-defined variables: these
are the targets of conversion from source. (For those used to C: one can
prefix the names of numerical variables with & but this is not required.)

Literal text in format is matched against source. Conversion specifiers
start with %, and recognized conversions include %f, %g or %lf for
floating-point numbers; %d for integers; %s for strings; and %m for
matrices. You may insert a positive integer after the percent sign: this
sets the maximum number of characters to read for the given conversion (or
the maximum number of rows in the case of matrix conversion). Alternatively,
you can insert a literal * after the percent to suppress the conversion
(thereby skipping any characters that would otherwise have been converted
for the given type). For example, %3d converts the next 3 characters in
source to an integer, if possible; %*g skips as many characters in source as
could be converted to a single floating-point number.

Matrix conversion works thus: the scanner reads a line of input and counts
the (space- or tab-separated) number of numeric fields. This defines the
number of columns in the matrix. By default, reading then proceeds for as
many lines (rows) as contain the same number of numeric columns, but the
maximum number of rows to read can be limited as described above.

In addition to %s conversion for strings, a simplified version of the C
format %N[chars] is available. In this format N is the maximum number of
characters to read and chars is a set of acceptable characters, enclosed in
square brackets: reading stops if N is reached or if a character not in
chars is encountered. The function of chars can be reversed by giving a
circumflex, ^, as the first character; in that case reading stops if a
character in the given set is found. (Unlike C, the hyphen does not play a
special role in the chars set.)

If the source string does not (fully) match the format, the number of
conversions may fall short of the number of arguments given. This is not in
itself an error so far as gretl is concerned. However, you may wish to check
the number of conversions performed; this is given by the built-in scalar
variable $nscan, whose value is refreshed every time sscanf is called. The
number of conversions is also printed, in interactive use.

Some examples follow:

	scalar x
	scalar y
	sscanf "123456", "%3d%3d", x, y

	sprintf S "1 2 3 4\n5 6 7 8"
	S
	matrix m
	sscanf S, "%m", m
	print m

# store Dataset

Arguments:  ficheiro-de-dados [ lista-de-variáveis ] 
Options:    --csv (use CSV format)
            --omit-obs (see below, on CSV format)
            --gnu-octave (use GNU Octave format)
            --gnu-R (use GNU R format)
            --traditional (use traditional ESL format)
            --gzipped (apply gzip compression)
            --jmulti (use JMulti ASCII format)
            --dat (use PcGive ASCII format)
            --database (use gretl database format)
            --overwrite (see below, on database format)

Saves either the entire dataset or, if a lista-de-variáveis is supplied, a
specified subset of the variables in the current dataset, to the file given
by datafile.

By default the data are saved in "native" gretl format, but the option flags
permit saving in several alternative formats. CSV (Comma-Separated Values)
data may be read into spreadsheet programs, and can also be manipulated
using a text editor. The formats of Octave, R and PcGive are designed for
use with the respective programs. Gzip compression may be useful for large
datasets. See the Gretl User's Guide for details on the various formats.

The option flag --omit-obs is applicable only when saving data in CSV
format. By default, if the data are time series or panel or if the dataset
includes specific observation markers, the CSV file includes a first column
identifying the observations (e.g. by date). If the --omit-obs flag is given
this column is omitted; only the actual data are printed.

Note that any scalar variables will not be saved automatically: if you wish
to save scalars you must explicitly list them in lista-de-variáveis.

The option of saving in gretl database format is intended to help with the
construction of large sets of series, possibly having mixed frequencies and
ranges of observations. At present this option is available only for annual,
quarterly or monthly time-series data. If you save to a file that already
exists, the default action is to append the newly saved series to the
existing content of the database. In this context it is an error if one or
more of the variables to be saved has the same name as a variable that is
already present in the database. The --overwrite flag has the effect that,
if there are variable names in common, the newly saved variable replaces the
variable of the same name in the original dataset.

Menu path:    /Ficheiro/Save data; /File/Export data

# summary Statistics

Argument:   [ lista-de-variáveis ] 

Print summary statistics for the variables in lista-de-variáveis, or for
all the variables in the data set if lista-de-variáveis is omitted. Output
consists of the mean, standard deviation (sd), coefficient of variation (=
sd/mean), median, minimum, maximum, skewness coefficient, and excess
kurtosis.

Menu path:    /View/Summary statistics
Other access: Menu de contexto da janela principal

# system Estimation

Variants:   system method=estimator
            system name=sysname
Argument:   savevars 
Examples:   system name="Klein Model 1"
            system method=sur
            system method=sur save=resids
            system method=3sls save=resids,fitted
            See also klein.inp, kmenta.inp

Starts a system of equations. Either of two forms of the command may be
given, depending on whether you wish to save the system for estimation in
more than one way or just estimate the system once.

To save the system you should give it a name, as in the first example (if
the name contains spaces it must be surrounded by double quotes). In this
case you estimate the system using the "estimate" command. With a saved
system of equations, you are able to impose restrictions (including
cross-equation restrictions) using the "restrict" command.

Alternatively you can specify an estimator for the system using method=
followed by a string identifying one of the supported estimators: "ols"
(Ordinary Least Squares), "tsls" (Two-Stage Least Squares) "sur" (Seemingly
Unrelated Regressions), "3sls" (Three-Stage Least Squares), "fiml" (Full
Information Maximum Likelihood) or "liml" (Limited Information Maximum
Likelihood). In this case the system is estimated once its definition is
complete.

An equation system is terminated by the line "end system". Within the system
four sorts of statement may be given, as follows.

  "equation": specify an equation within the system. At least two such
  statements must be provided.

  "instr": for a system to be estimated via Three-Stage Least Squares, a
  list of instruments (by variable name or number). Alternatively, you can
  put this information into the "equation" line using the same syntax as in
  the "tsls" command.

  "endog": for a system of simultaneous equations, a list of endogenous
  variables. This is primarily intended for use with FIML estimation, but
  with Three-Stage Least Squares this approach may be used instead of giving
  an "instr" list; then all the variables not identified as endogenous will
  be used as instruments.

  "identity": for use with FIML, an identity linking two or more of the
  variables in the system. This sort of statement is ignored when an
  estimator other than FIML is used.

In the optional "save=" field of the command you can specify whether to save
the residuals ("resids") and/or the fitted values ("fitted").

For examples of the specification and estimation of systems of equations,
please see the scripts klein.inp, kmenta.inp and greene14_2.inp (supplied
with the gretl distribution).

Menu path:    /Model/Simultaneous equations

# tabprint Printing

Argument:   [ -f filename ] 
Options:    --complete (Create a complete document)
            --format="f1|f2|f3|f4" (Specify a custom format)

Must follow the estimation of a model. Prints the estimated model in the
form of a LaTeX table. If a filename is specified using the -f flag output
goes to that file, otherwise it goes to a file with a name of the form
model_N.tex, where N is the number of models estimated to date in the
current session. See also "eqnprint".

If the --complete flag is given the LaTeX file is a complete document, ready
for processing; otherwise it must be included in a document.

If you wish alter the appearance of the tabular output, you can specify a
custom row format using the --format flag. The format string must be
enclosed in double quotes and must be tied to the flag with an equals sign.
The pattern for the format string is as follows. There are four fields,
representing the coefficient, standard error, t-ratio and p-value
respectively. These fields should be separated by vertical bars; they may
contain a printf-type specification for the formatting of the numeric value
in question, or may be left blank to suppress the printing of that column
(subject to the constraint that you can't leave all the columns blank). Here
are a few examples:

	--format="%.4f|%.4f|%.4f|%.4f"
	--format="%.4f|%.4f|%.3f|"
	--format="%.5f|%.4f||%.4f"
	--format="%.8g|%.8g||%.4f"

The first of these specifications prints the values in all columns using 4
decimal places. The second suppresses the p-value and prints the t-ratio to
3 places. The third omits the t-ratio. The last one again omits the t, and
prints both coefficient and standard error to 8 significant figures.

Once you set a custom format in this way, it is remembered and used for the
duration of the gretl session. To revert to the default format you can use
the special variant --format=default.

Menu path:    Janela do modelo, /LaTeX

# testuhat Tests

Must follow a model estimation command. Gives the frequency distribution for
the residual from the model along with a chi-square test for normality,
based on the procedure suggested by Doornik and Hansen (1984).

Menu path:    Janela do modelo, /Testes/Normality of residual

# tobit Estimation

Arguments:  variável-dependente variáveis-independentes 
Options:    --vcv (mostrar matriz de covariância)
            --verbose (mostrar detalhes das iterações)

Estimates a Tobit model. This model may be appropriate when the dependent
variable is "censored". For example, positive and zero values of purchases
of durable goods on the part of individual households are observed, and no
negative values, yet decisions on such purchases may be thought of as
outcomes of an underlying, unobserved disposition to purchase that may be
negative in some cases. For details see Greene's Econometric Analysis,
Chapter 20.

Menu path:    /Model/Nonlinear models/Tobit

# tsls Estimation

Arguments:  variável-dependente variáveis-independentes ; instruments 
Options:    --vcv (mostrar matriz de covariância)
            --robust (robust standard errors)
Example:    tsls y1 0 y2 y3 x1 x2 ; 0 x1 x2 x3 x4 x5 x6

Computes two-stage least squares (TSLS or IV) estimates: depvar is the
dependent variable, variáveis-independentes is the list of independent
variables (including right-hand side endogenous variables) in the structural
equation for which TSLS estimates are needed; and instruments is the
combined list of exogenous and predetermined variables in all the equations.
If the instruments list is not at least as long as variáveis-independentes,
the model is not identified.

In the above example, the ys are the endogenous variables and the xs are the
exogenous and predetermined variables.

Output includes the Hausman test and, if the model is over-identified, the
Sargan over-identification test. In the Hausman test, the null hypothesis is
that OLS estimates are consistent, or in other words estimation by means of
instrumental variables is not required. A model of this sort is
over-identified if there are more instruments than are strictly required.
The Sargan test is based on an auxiliary regression of the residuals from
the two-stage least squares model on the full list of instruments. The null
hypothesis is that all the instruments are valid, and suspicion is thrown on
this hypothesis if the auxiliary regression has a significant degree of
explanatory power. Davidson and MacKinnon (2004, chapter 8) give a good
explanation of both tests.

Menu path:    /Model/Other linear models/Two-Stage least Squares

# var Estimation

Arguments:  ordem lista-de-variáveis [ ; exolist ] 
Options:    --nc (não incluir uma constante)
            --seasonals (incluir variáveis sazonais auxiliares)
            --robust (robust standard errors)
            --impulse-responses (print impulse responses)
            --variance-decomp (print forecast variance decompositions)
            --lagselect (show information criteria for lag selection)
Examples:   var 4 x1 x2 x3 ; time mydum
            var 4 x1 x2 x3 --seasonals
            var 12 x1 x2 x3 --lagselect

Sets up and estimates (using OLS) a vector autoregression (VAR). The first
argument specifies the lag order -- or the maximum lag order in case the
--lagselect option is given (see below). The order may be given numerically,
or as the name of a pre-existing scalar variable. Then follows the setup for
the first equation. Don't include lags among the elements of
lista-de-variáveis -- they will be added automatically. The semi-colon
separates the stochastic variables, for which order lags will be included,
from any exogenous variables in exolist. Note that a constant is included
automatically unless you give the --nc flag, a trend can be added with the
--trend flag, and seasonal dummy variables may be added using the
--seasonals flag.

A separate regression is run for each variable in lista-de-variáveis.
Output for each equation includes F-tests for zero restrictions on all lags
of each of the variables, an F-test for the significance of the maximum lag,
and, if the --impulse-responses flag is given, forecast variance
decompositions and impulse responses.

Forecast variance decompositions and impulse responses are based on the
Cholesky decomposition of the contemporaneous covariance matrix, and in this
context the order in which the (stochastic) variables are given matters. The
first variable in the list is assumed to be "most exogenous" within-period.
The horizon for variance decompositions and impulse responses can be set
using the "set" command.

If the --lagselect option is given, the first parameter to the var command
is taken as the maximum lag order. Output consists of a table showing the
values of the Akaike (AIC), Schwartz (BIC) and Hannan-Quinn (HQC)
information criteria computed from VARs of order 1 to the given maximum.
This is intended to help with the selection of the optimal lag order. The
usual VAR output is not presented.

Menu path:    /Modelo/Série temporal/Vector autoregression

# varlist Dataset

Prints a listing of variables currently available. "list" and "ls" are
synonyms.

# vartest Tests

Arguments:  var1 var2 

Calculates the F statistic for the null hypothesis that the population
variances for the variables var1 and var2 are equal, and shows its p-value.

Menu path:    /Model/Bivariate tests/Difference of variances

# vecm Estimation

Arguments:  ordem rank lista-de-variáveis 
Options:    --nc (sem constante)
            --rc (constante restringida)
            --crt (constante e tendência restringida)
            --ct (constante e tendência não restringida)
            --seasonals (incluir auxiliares sazonais centradas)
            --impulse-responses (print impulse responses)
            --variance-decomp (print forecast variance decompositions)
Examples:   vecm 4 1 Y1 Y2 Y3
            vecm 3 2 Y1 Y2 Y3 --rc
            See also denmark.inp, hamilton.inp

A VECM is a form of vector autoregression or VAR (see "var"), applicable
where the variables in the model are individually integrated of order 1
(that is, are random walks, with or without drift), but exhibit
cointegration. This command is closely related to the Johansen test for
cointegration (see "coint2").

The order parameter to this command represents the lag order of the VAR
system. The number of lags in the VECM itself (where the dependent variable
is given as a first difference) is one less than order.

The rank parameter represents the cointegration rank, or in other words the
number of cointegrating vectors. This must be greater than zero and less
than or equal to (generally, less than) the number of endogenous variables
given in lista-de-variáveis.

lista-de-variáveis supplies the list of endogenous variables, in levels.
The inclusion of deterministic terms in the model is controlled by the
option flags. The default if no option is specified is to include an
"unrestricted constant", which allows for the presence of a non-zero
intercept in the cointegrating relations as well as a trend in the levels of
the endogenous variables. In the literature stemming from the work of
Johensen (see for example his 1995 book) this is often referred to as "case
3". The first four options given above, which are mutually exclusive,
produce cases 1, 2, 4 and 5 respectively. The meaning of these cases and the
criteria for selecting a case are explained in the Gretl User's Guide.

The --seasonals option, which may be combined with any of the other options,
specifies the inclusion of a set of centered seasonal dummy variables. This
option is available only for quarterly or monthly data.

The first example above specifies a VECM with lag order 4 and a single
cointegrating vector. The endogenous variables are Y1, Y2 and Y3. The second
example uses the same variables but specifies a lag order of 3 and two
cointegrating vectors; it also specifies a "restricted constant", which is
appropriate if the cointegrating vectors may have a non-zero intercept but
the Y variables have no trend.

Menu path:    /Modelo/Série temporal/VECM

# vif Tests

Must follow the estimation of a model which includes at least two
independent variables. Calculates and displays the Variance Inflation
Factors (VIFs) for the regressors. The VIF for regressor j is defined as

  1/(1 - Rj^2)

where R_j is the coefficient of multiple correlation between regressor j and
the other regressors. The factor has a minimum value of 1.0 when the
variable in question is orthogonal to the other independent variables.
Neter, Wasserman, and Kutner (1990) suggest inspecting the largest VIF as a
diagnostic for collinearity; a value greater than 10 is sometimes taken as
indicating a problematic degree of collinearity.

Menu path:    Janela do modelo, /Testes/Collinearity

# wls Estimation

Arguments:  wtvar variável-dependente variáveis-independentes 
Options:    --vcv (mostrar matriz de covariância)
            --robust (robust standard errors)
            --quiet (não mostrar resultados da regressão)

Computes weighted least squares (WLS) estimates using wtvar as the weight,
depvar as the dependent variable, and variáveis-independentes as the list
of independent variables. Let w denote the positive square root of wtvar;
then WLS is basically equivalent to an OLS regression of w * depvar on w *
variáveis-independentes. The R-squared, however, is calculated in a special
manner, namely as

  R^2 = 1 - ESS / WTSS

where ESS is the error sum of squares (sum of squared residuals) from the
weighted regression and WTSS denotes the "weighted total sum of squares",
which equals the sum of squared residuals from a regression of the weighted
dependent variable on the weighted constant alone.

If wtvar is a dummy variable, WLS estimation is equivalent to eliminating
all observations with value zero for wtvar.

Menu path:    /Model/Other linear models/Weighted Least Squares

# xcorrgm Statistics

Arguments:  var1 var2 [ maxlag ] 
Example:    xcorrgm x y 12

Prints and graphs the cross-correlogram for variables var1 and var2, which
may be specified by name or number. The values are the sample correlation
coefficients between the current value of var1 and successive leads and lags
of var2.

If a maxlag value is specified the length of the cross-correlogram is
limited to at most that number of leads and lags, otherwise the length is
determined automatically, as a function of the frequency of the data and the
number of observations.

Menu path:    /View/Cross-correlogram
Other access: Menu de contexto da janela principal (multiple selection)

# xtab Statistics

Arguments:  ylist ; xlist 
Options:    --row (display row percentages)
            --column (display column percentages)
            --zeros (display zero entries)

With no options given, displays a contingency table or cross-tabulation for
each variable in ylist (by row) against each variable in xlist (by column).
Variables in these lists can be referenced by name or by number. Note that
all the variables must have been marked as discrete.

The --row and --column options are mutually exclusive, and instead of the
frequency count yield the percentages for each row or column, respectively.
The --zeros option may be useful for importing the table into another
program, such as a spreadsheet.

Pearson's chi-square test for independence is displayed if the expected
frequency under independence is at least 1.0e-7 for all cells. A common rule
of thumb for the validity of this statistic is that at least 80 percent of
cells should have expected frequencies of 5 or greater; if this criterion is
not met a warning is printed.

