/usr/share/gretl/gretlcli.hlp.pt

headings 10
Tests 24
add
adf
arch
chow
chow
coeffsum
coint
coint2
cusum
difftest
hausman
kpss
leverage
levinlin
meantest
modtest
normtest
omit
qlrtest
reset
restrict
runs
vartest
vif
Statistics 13
anova
corr
corrgm
fractint
freq
hurst
mahal
pca
pergm
spearman
summary
xcorrgm
xtab
Dataset 16
append
data
dataset
delete
genr
info
labels
nulldata
open
rename
setinfo
setobs
setmiss
smpl
store
varlist
Estimation 33
ar
ar1
arbond
arima
biprobit
dpanel
duration
equation
estimate
garch
gmm
heckit
hsk
intreg
kalman
lad
logistic
logit
mle
mpols
negbin
nls
ols
panel
poisson
probit
quantreg
system
tobit
tsls
var
vecm
wls
Graphs 7
boxplot
gnuplot
graphpg
qqplot
rmplot
scatters
textplot
Programming 19
break
clear
catch
clear
debug
elif
else
end
endif
endloop
foreign
function
if
include
loop
makepkg
run
set
sscanf
Utilities 5
help
modeltab
pvalue
quit
shell
Transformations 9
diff
discrete
dummify
lags
ldiff
logs
orthdev
sdiff
square
Printing 7
eqnprint
modprint
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

# anova Statistics

Arguments: response treatment [ block ]
Option: --quiet (don't print results)

Analysis of Variance: response is a series measuring some effect of interest
and treatment must be a discrete variable that codes for two or more types
of treatment (or non-treatment). For two-way ANOVA, the block variable
(which should also be discrete) codes for the values of some control
variable.

Unless the --quiet option is given, this command prints a table showing the
sums of squares and mean squares along with an F-test. The F-test and its
P-value can be retrieved using the accessors $test and $pvalue respectively.

The null hypothesis for the F-test is that the mean response is invariant
with respect to the treatment type, or in words that the treatment has no
effect. Strictly speaking, the test is valid only if the variance of the
response is the same for all treatment types.

Note that the results shown by this command are in fact a subset of the
information given by the following procedure, which is easily implemented in
gretl. Create a set of dummy variables coding for all but one of the
treatment types. For two-way ANOVA, in addition create a set of dummies
coding for all but one of the "blocks". Then regress response on a constant
and the dummies using "ols". For a one-way design the ANOVA table is printed
via the --anova option to ols. In the two-way case the relevant F-test is
found by using the "omit" command. For example (assuming y is the response,
xt codes for the treatment, and xb codes for blocks):

# one-way
list dxt = dummify(xt)
ols y 0 dxt --anova
# two-way
list dxb = dummify(xb)
ols y 0 dxt dxb
# test joint significance of dxt
omit dxt --quiet

Menu path: /Model/Other linear models/ANOVA

# 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)

# biprobit Estimation

Arguments: depvar1 depvar2 indepvars1 [ ; indepvars2 ]
Options: --vcv (print covariance matrix)
--robust (robust standard errors)
--opg (see below)
--save-xbeta (see below)
--verbose (print extra information)
Examples: biprobit y1 y2 0 x1 x2
biprobit y1 y2 0 x11 x12 ; 0 x21 x22
See also biprobit.inp

Estimates a bivariate probit model, using the Newton-Raphson method to
maximize the likelihood.

The argument list starts with the two (binary) dependent variables, followed
by a list of regressors. If a second list is given, separated by a
semicolon, this is interpreted as a set of regressors specific to the second
equation, with indepvars1 being specific to the first equation; otherwise
indepvars1 is taken to represent a common set of regressors.

By default, standard errors are computed using a numerical approximation to
the Hessian at convergence. But if the --opg option is given the covariance
matrix is based on the Outer Product of the Gradient (OPG), or if the
--robust option is given QML standard errors are calculated, using a
"sandwich" of the inverse of the Hessian and the OPG.

After successful estimation, the accessor $uhat retrieves a matrix with two
columns holding the generalized residuals for the two equations; that is,
the expected values of the disturbances conditional on the observed outcomes
and covariates. By default $yhat retrieves a matrix with four columns,
holding the estimated probabilities of the four possible joint outcomes for
(y_1, y_2), in the order (1,1), (1,0), (0,1), (0,0). Alternatively, if the
option --save-xbeta is given, $yhat has two columns and holds the values of
the index functions for the respective equations.

The output includes a likelihood ratio test of the null hypothesis that the
disturbances in the two equations are uncorrelated.

# 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

# clear Programming

Options: --dataset (clear dataset only)
--other (clear everything but the dataset)

With no options, clears all saved objects, including the current dataset if
any, out of memory. Note that opening a new dataset, or using the "nulldata"
command to create an empty dataset, also has this effect, so use of "clear"
is not usually necessary.

If the --dataset option is given, then only the dataset is cleared; other
saved objects such as named matrices and scalars are preserved.

If the --other option is given, then the current dataset, if any, is
preserved but all other saved objects are destroyed.

# catch Programming

Syntax:
catch command

This is not a command in its own right but can be used as a prefix to most
regular commands: the effect is to prevent termination of a script if an
error occurs in executing the command. If an error does occur, this is
registered in an internal error code which can be accessed as $error (a zero
value indicates success). The value of $error should always be checked
immediately after using catch, and appropriate action taken if the command
failed.

The catch keyword cannot be used before if, elif or endif.

# chow Tests

Variants: chow obs
chow dummyvar --dummy
Options: --dummy (use a pre-existing dummy variable)
--quiet (don't print estimates for augmented model)
Examples: chow 25
chow 1988:1
chow female --dummy

Must follow an OLS regression. If an observation number or date is given,
provides a test for the null hypothesis of no structural break at the given
split point. The procedure is to create a dummy variable which equals 1 from
the split point specified by obs to the end of the sample, 0 otherwise, and
also interaction terms between this dummy and the original regressors. If a
dummy variable is given, tests the null hypothesis of structural homogeneity
with respect to that dummy. Again, interaction terms are added. In either
case an augmented regression is run including the additional terms.

By default an F statistic is calculated, taking the augmented regression as
the unrestricted model and the original as the restricted. But if the
original model used a robust estimator for the covariance matrix, the test
statistic is a Wald chi-square value based on a robust estimator of the
covariance matrix for the augmented regression.

Menu path: Model window, /Tests/Chow test

# clear Programming

Options: --dataset (clear dataset only)
--other (clear everything but the dataset)

If the --dataset option is given, then only the dataset is cleared; other
saved objects such as named matrices and scalars are preserved.

If the --other option is given, then the current dataset, if any, is
preserved but all other saved objects are destroyed.

# 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)

# 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: varlist
Option: --quiet (don't report results except on error)

Reads the variables in varlist 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: /File/Databases

# dataset Dataset

Arguments: keyword parameters
Examples: dataset addobs 24
dataset insobs 10
dataset compact 1
dataset compact 4 last
dataset expand interp
dataset transpose
dataset sortby x1
dataset resample 500
dataset renumber x 4
dataset clear

Performs various operations on the data set as a whole, depending on the
given keyword, which must be addobs, insobs, clear, compact, expand,
transpose, sortby, dsortby, resample or renumber. Note: with the exception
of clear, 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.

insobs: Must be followed by a positive integer no greater than the current
number of observations. Inserts a single observation at the specified
position. All subsequent data are shifted by one place and the dataset is
extended by one observation. All variables apart from the constant are given
missing values at the new observation. This action is not available for
panel datasets.

clear: No parameter required. Clears out the current data, returning gretl
to its initial "empty" state.

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: This command is only available for annual or quarterly time series
data: annual data can be expanded to quarterly, and quarterly data to
monthly frequency. By default all the series in the data set are padded out
to the new frequency by repeating the existing values, but if the modifier
interp is appended then the series are expanded using Chow-Lin
interpolation: the regressors are a constant and quadratic trend and an
AR(1) disturbance process is assumed.

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: The name of a single series or list is required. If one series is
given, the observations on all variables in the dataset are re-ordered by
increasing value of the specified series. If a list is given, the sort
proceeds hierarchically: if the observations are tied in sort order with
respect to the first key variable then the second key is used to break the
tie, and so on until the tie is broken or the keys are exhausted. Note that
this command is available only for undated data.

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

resample: Constructs a new dataset by random sampling, with replacement, of
the rows of the current dataset. One argument is required, namely the number
of rows to include. This may be less than, equal to, or greater than the
number of observations in the original data. The original dataset can be
retrieved via the command smpl full.

renumber: Requires the name of an existing series followed by an integer
between 1 and the number of series in the dataset minus one. Moves the
specified series to the specified position in the dataset, renumbering the
other series accordingly. (Position 0 is occupied by the constant, which
cannot be moved.)

Menu path: /Data

# debug Programming

Argument: function

Experimental debugger for user-defined functions, available in the
command-line program, gretlcli, and in the GUI console. The debug command
should be invoked after the function in question is defined but before it is
called. The effect is that execution pauses when the function is called and
a special prompt is shown.

At the debugging prompt you can type next to execute the next command in the
function, or continue to allow execution of the function to continue
unimpeded. These commands can be abbreviated as n and c respectively. You
can also interpolate an instruction at this prompt, for example a print
command to reveal the current value of some variable of interest.

# delete Dataset

Argument: varname
Option: --db (delete series from database)

This command is an all-purpose destructor for named variables (whether
series, scalars, matrices, strings or bundles). It should be used with
caution; no confirmation is asked.

In the case of series, varname may take the form of a named list, in which
case all series in the list are deleted, or it may take the form of an
explicit list of series given by name or ID number. Note that when you
delete series any series with higher ID numbers than those on the deletion
list will be re-numbered.

If the --db option is given, this command deletes the listed series not from
the current dataset but from a gretl database, assuming that a database has
been opened, and the user has write permission for file in question. See
also the "open" command.

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

# diff Transformations

Argument: varlist

The first difference of each variable in varlist 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: varlist
Option: --reverse (mark variables as continuous)

Marks each variable in varlist 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 varlist are marked as being continuous.

Menu path: /Variable/Edit attributes

# dpanel Estimation

Argument: p ; depvar indepvars [ ; instruments ]
Options: --quiet (don't show estimated model)
--vcv (print covariance matrix)
--two-step (perform 2-step GMM estimation)
--system (add equations in levels)
--time-dummies (add time dummy variables)
--dpdstyle (emulate DPD package for Ox)
--asymptotic (uncorrected asymptotic standard errors)
Examples: dpanel 2 ; y x1 x2
dpanel 2 ; y x1 x2 --system
dpanel {2 3} ; y x1 x2 ; x1
dpanel 1 ; y x1 x2 ; x1 GMM(x2,2,3)
See also bbond98.inp

Carries out estimation of dynamic panel data models (that is, panel models
including one or more lags of the dependent variable) using either the
GMM-DIF or GMM-SYS method.

The parameter p represents the order of the autoregression for the dependent
variable. In the simplest case this is a scalar value, but a pre-defined
matrix may be given for this argument, to specify a set of (possibly
non-contiguous) lags to be used.

The dependent variable and regressors should be given in levels form; they
will be differenced automatically (since this estimator uses differencing to
cancel out the individual effects).

The last (optional) field in the command is for specifying instruments. If
no instruments are given, it is assumed that all the independent variables
are strictly exogenous. If you specify any instruments, you should include
in the list any strictly exogenous independent variables. For predetermined
regressors, you can use the GMM function to include a specified range of
lags in block-diagonal fashion. This is illustrated in the third example
above. The first argument to GMM is the name of the variable in question,
the second is the minimum lag to be used as an instrument, and the third is
the maximum lag. The same syntax can be used with the GMMlevel function to
specify GMM-type instruments for the equations in levels.

By default the results of 1-step estimation are reported (with robust
standard errors). You may select 2-step estimation as an option. In both
cases tests for autocorrelation of orders 1 and 2 are provided, as well as
the Sargan overidentification test and a Wald test for the joint
significance of the regressors. Note that in this differenced model
first-order autocorrelation is not a threat to the validity of the model,
but second-order autocorrelation violates the maintained statistical
assumptions.

In the case of 2-step estimation, standard errors are by default computed
using the finite-sample correction suggested by Windmeijer (2005). The
standard asymptotic standard errors associated with the 2-step estimator are
generally reckoned to be an unreliable guide to inference, but if for some
reason you want to see them you can use the --asymptotic option to turn off
the Windmeijer correction.

If the --time-dummies option is given, a set of time dummy variables is
added to the specified regressors. The number of dummies is one less than
the maximum number of periods used in estimation, to avoid perfect
collinearity with the constant. The dummies are entered in differenced form
unless the --dpdstyle option is given, in which case they are entered in
levels.

For further details and examples, please see the Gretl User's Guide.

Menu path: /Model/Panel/Dynamic panel model

# dummify Transformations

Argument: varlist
Options: --drop-first (omit lowest value from encoding)
--drop-last (omit highest value from encoding)

For any suitable variables in varlist, 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)

# duration Estimation

Arguments: depvar indepvars [ ; censvar ]
Options: --exponential (use exponential distribution)
--loglogistic (use log-logistic distribution)
--lognormal (use log-normal distribution)
--medians (fitted values are medians)
--robust (robust (QML) standard errors)
--vcv (print covariance matrix)
--verbose (print details of iterations)
Examples: duration y 0 x1 x2
duration y 0 x1 x2 ; cens

Estimates a duration model: the dependent variable (which must be positive)
represents the duration of some state of affairs, for example the length of
spells of unemployment for a cross-section of respondents. By default the
Weibull distribution is used but the exponential, log-logistic and
log-normal distributions are also available.

If some of the duration measurements are right-censored (e.g. an
individual's spell of unemployment has not come to an end within the period
of observation) then you should supply the trailing argument censvar, a
series in which non-zero values indicate right-censored cases.

By default the fitted values obtained via the accessor $yhat are the
conditional means of the durations, but if the --medians option is given
then $yhat provides the conditional medians instead.

Please see the Gretl User's Guide for details.

Menu path: /Model/Nonlinear models/Duration data...

# elif Programming

See "if".

# else Programming

See "if". Note that "else" requires a line to itself, before the following
conditional command. You can append a comment, as in

else # OK, do something different

But you cannot append a command, as in

else x = 5 # wrong!

# 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: Model window, /LaTeX

# equation Estimation

Arguments: depvar indepvars
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)
--quiet (don't print results)
--verbose (print details of iterations)
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=. These arguments are optional if
the system in question has already been estimated and occupies the place of
the "last model"; in that case the estimator defaults to the previously used
value.

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.

If the verbose option is given and an iterative method is specified, details
of the iterations are printed.

# fcast Prediction

Arguments: [ startobs endobs ] [ steps-ahead ] [ varname ]
Options: --dynamic (create dynamic forecast)
--static (create static forecast)
--out-of-sample (generate post-sample forecast)
--no-stats (don't print forecast statistics)
--quiet (don't print anything)
--rolling (see below)
--plot[=filename] (see below)
Examples: fcast 1997:1 2001:4 f1
fcast fit2
fcast 2004:1 2008:3 4 rfcast --rolling

Must follow an estimation command. Forecasts are generated for a certain
range of observations: if startobs and endobs are given, for that range (if
possible); otherwise if the --out-of-sample option is given, for
observations following the range over which the model was estimated;
otherwise over the currently defined sample range. If an out-of-sample
forecast is requested but no relevant observations are available, an error
is flagged. Depending on the nature of the model, standard errors may also
be generated; see below. Also see below for the special effect of the
--rolling option.

If the last model estimated is a single equation, then the optional varname
argument has the following effect: the forecast values are not printed, but
are saved to the dataset under the given name. If the last model is a system
of equations, varname has a different effect, namely selecting a particular
endogenous variable for forecasting (the default being to produce forecasts
for all the endogenous variables). In the system case, or if varname is not
given, the forecast values can be retrieved using the accessor $fcast, and
the standard errors, if available, via $fcerr.

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. Static forecasts
are one step ahead, based on realized values from the previous period, while
dynamic forecasts employ the chain rule of forecasting. For example, if a
forecast for y in 2008 requires as input a value of y for 2007, a static
forecast is impossible without actual data for 2007. A dynamic forecast for
2008 is possible if a prior forecast can be substituted for y in 2007.

The default is to give a static forecast for any portion of the forecast
range that lies within the sample range over which the model was estimated,
and a dynamic forecast (if relevant) out of sample. The dynamic option
requests a dynamic forecast from the earliest possible date, and the static
option requests a static forecast even out of sample.

The rolling option is presently available only for single-equation models
estimated via OLS. When this option is given the forecasts are recursive.
That is, each forecast is generated from an estimate of the given model
using data from a fixed starting point (namely, the start of the sample
range for the original estimation) up to the forecast date minus k, where k
is the number of steps ahead, which must be given in the steps-ahead
argument. The forecasts are always dynamic if this is applicable. Note that
the steps-ahead argument should be given only in conjunction with the
rolling option.

The --plot option (available only in the case of single-equation estimation)
calls for a plot file to be produced, containing a graphical representation
of the forecast. When no filename parameter is given, gretl writes a gnuplot
command file with a name on the pattern gpttmp01.plt to the user's gretl
working directory (with the number incremented for successive plots). If a
filename is appended, its extension is used to determine the type of file to
be written (.eps for EPS, .pdf for PDF, or .png for PNG; any other extension
gives a gnuplot command file). For example,

fcast --plot=fc.pdf

will generate a graphic in PDF format. Absolute pathnames are respected,
otherwise files are written to the gretl working directory.

The nature of the forecast standard errors (if available) depends on the
nature of the model and the forecast. For static linear models standard
errors are computed using the method outlined by Davidson and MacKinnon
(2004); they incorporate both uncertainty due to the error process and
parameter uncertainty (summarized in the covariance matrix of the parameter
estimates). For dynamic models, forecast standard errors are computed only
in the case of a dynamic forecast, and they do not incorporate parameter
uncertainty. For nonlinear models, forecast standard errors are not
presently available.

Menu path: Model window, /Analysis/Forecasts

# foreign Programming

Syntax: foreign language=lang
Options: --send-data (pre-load the current dataset; see below)
--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 three "foreign" programs are supported in this way, GNU R
(language=R), Jurgen Doornik's Ox (language=Ox) and GNU Octave
(language=Octave). Language names are recognized on a case-insensitive
basis.

The --send-data option is valid only in connection with R and Octave; it has
the effect of making the entire current gretl dataset available within the
target program, under the name gretldata.

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

# fractint Statistics

Arguments: series [ order ]
Options: --gph (do Geweke and Porter-Hudak test)
--all (do both tests)
--quiet (don't print results)

Tests the specified series for fractional integration ("long memory"). The
null hypothesis is that the integration order of the series is zero. By
default the local Whittle estimator (Robinson, 1995) is used but if the
--gph option is given the GPH test (Geweke and Porter-Hudak, 1983) is
performed instead. If the --all flag is given then the results of both tests
are printed.

For details on this sort of test, see Phillips and Shimotsu (2004).

If the optional order argument is not given the order for the test(s) is set
automatically as the lesser of T/2 and T^0.6.

The results can be retrieved using the accessors $test and $pvalue. These
values are based on the Local Whittle Estimator unless the --gph option is
given.

Menu path: /Variable/Unit root tests/Fractional integration

# freq Statistics

Argument: series
Options: --nbins=n (specify number of bins)
--min=minval (specify minimum, see below)
--binwidth=width (specify bin width, see below)
--quiet (suppress printing of graph)
--normal (test for the normal distribution)
--gamma (test for gamma distribution)
--silent (don't print anything)
--show-plot (see below)
Examples: freq x
freq x --normal
freq x --nbins=5
freq x --min=0 --binwidth=0.10

With no options given, displays the frequency distribution for series (given
by name or number), with the number of bins and their size chosen
automatically.

To control the presentation of the distribution you may specify either the
number of bins or the minimum value plus the width of the bins, as shown in
the last two examples above. The --min option sets the lower limit of the
left-most bin.

If the --normal option is given, the Doornik-Hansen chi-square test for
normality is computed. 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). Note that the parameterization of the gamma distribution
used in gretl is (shape, scale).

In interactive mode a graph of the distribution is displayed by default. The
--quiet flag can be used to suppress this. Conversely, the graph is not
usually shown when the "freq" is used in a script, but you can force its
display by giving the --show-plot option. (This does not apply when using
the command-line program, gretlcli.)

The --silent flag suppresses the usual output entirely. This makes sense
only in conjunction with one or other of the distribution test options: the
test statistic and its p-value are recorded, and can be retrieved using the
accessors $test and $pvalue.

Menu path: /Variable/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 ; depvar [ indepvars ]
Options: --robust (robust standard errors)
--verbose (print details of iterations)
--vcv (print covariance matrix)
--nc (do not include a constant)
--stdresid (standardize the residuals)
--fcp (use Fiorentini, Calzolari, Panattoni algorithm)
--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 indepvars 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 parameter p therefore represents the Generalized (or "AR") order, while
q represents the regular ARCH (or "MA") order. If p is non-zero, q must also
be non-zero otherwise the model is unidentified. However, you can estimate a
regular ARCH model by setting q to a positive value and p to zero. The sum
of p and q must be no greater than 5. Note that a constant is automatically
included in the mean equation unless the --nc option is given.

By default native gretl code is used in estimation of GARCH models, but you
also have the option of using the algorithm of Fiorentini, Calzolari and
Panattoni (1996). The former uses the BFGS maximizer while the latter uses
the information matrix to maximize the likelihood, with fine-tuning via the
Hessian.

Several variant estimators of the 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

If the --stdresid option is given, the $uhat values are divided by the
square root of h_t.

Menu path: /Model/Time series/GARCH

# genr Dataset

Arguments: newvar = formula

In the appropriate context, series, scalar and matrix are synonyms for this
command.

Creates new variables, often via transformations of existing variables. See
also "diff", "logs", "lags", "ldiff", "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.

A genr 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 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.

The genr command supports a wide range of mathematical and statistical
functions, including all the common ones plus several that are special to
econometrics. In addition it offers access to numerous internal variables
that are defined in the course of running regressions, doing hypothesis
tests, and so on. For a listing of functions and accessors, type "help
functions".

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. In
the case of quarterly data (periodicity 4), the program creates dq1 = 1
for first quarter and 0 in other quarters, dq2 = 1 for the second quarter
and 0 in other quarters, and so on. With monthly data the dummies are
named dm1, dm2, and so on. With other frequencies the names are dummy_1,
dummy_2, etc.

"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.

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 special variable obs serves as an index of the observations. For
instance genr dum = (obs=15) will generate a dummy variable that has value 1
for observation 15, 0 otherwise. You can also use this variable to pick out
particular observations by date or name. For example, genr d = (obs>1986:4),
genr d = (obs>"2008/04/01"), or genr d = (obs="CA"). If daily dates or
observation labels are used in this context, they should be enclosed in
double quotes. Quarterly and monthly dates (with a colon) may be used
unquoted. Note that in the case of annual time series data, the year is not
distinguishable syntactically from a plain integer; therefore if you wish to
compare observations against obs by year you must use the function obsnum to
convert the year to a 1-based index value, as in genr d =
(obs>obsnum(1986)).

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.

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 NA 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: /Add/Define new variable
Other access: Main window pop-up menu

# gmm Estimation

Options: --two-step (two step estimation)
--iterate (iterated GMM)
--vcv (print covariance matrix)
--verbose (print details of iterations)
--lbfgs (use L-BFGS-B instead of regular BFGS)

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 (typically 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. Any options should be appended to the end
gmm line.

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)
--time-series (plot against time)
--suppress-fitted (don't show fitted line)
--single-yaxis (force use of just one y-axis)
--linear-fit (show least squares fit)
--inverse-fit (show inverse fit)
--quadratic-fit (show quadratic fit)
--loess-fit (show loess fit)
--dummy (see below)
--matrix=name (plot columns of named matrix)
--output=filename (send output to specified file)
--input=filename (take input from specified file)
Examples: gnuplot y1 y2 x
gnuplot x --time-series --with-lines
gnuplot wages educ gender --dummy

The variables in the list yvars are graphed against xvar. For a time series
plot you may either give time as xvar or use the option flag --time-series.

If the --dummy option is selected, exactly three variables should be given:
a single y variable, an x variable, and dvar, a discrete variable. The
effect is to plot yvar against xvar with the points shown in different
colors depending on the value of dvar at the given observation.

Generally, yvars and xvar refer to series in the current dataset (given
either by name or ID number). But if a named matrix is supplied via the
--matrix option these arguments (which must be given as numerical values)
denote (1-based) column indices for the given matrix. So for example if you
want a scatterplot of column 2 of matrix M against column 1, you should do:

gnuplot 2 1 --matrix=M

In interactive mode the plot is displayed immediately. In batch mode the
default behavior is that a gnuplot command file is written in the user's
working directory, 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). This behavior can be modified by use of the --output=filename
option. This option controls the filename used, and at the same time allows
you to specify a particular output format via the three-letter extension of
the file name, as follows: .eps results in the production of an Encapulated
PostScript (EPS) file; .pdf produces PDF; .png produces PNG format, .emf
calls for EMF (Enhanced MetaFile), .fig calls for an Xfig file, and .svg for
SVG (Scalable Vector Graphics). If the dummy filename "display" is given
then the plot is shown on screen as in interactive mode. If a filename with
any extension other than those just mentioned is given, a gnuplot command
file is written.

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: Main window pop-up menu, graph button on toolbar

# graphpg Graphs

The session "graph page" will work only if you have the LaTeX typesetting
system installed, and are able to generate and view PDF or PostScript
output.

In the session icon window, you can drag up to eight graphs onto the graph
page icon. When you double-click on the graph page (or right-click and
select "Display"), a page containing the selected graphs will be composed
and opened in a suitable viewer. From there you should be able to print the
page.

To clear the graph page, right-click on its icon and select "Clear".

In script (or console) mode, you can add a graph to the graph page by
issuing the command graphpg add after saving a named graph, as in

grf1 <- gnuplot Y X
graphpg add

Also in script mode you can call for display of the graph page using the
command graphpg show, and can clear the page via graphpg free.

Note that on systems other than MS Windows, you may have to adjust the
setting for the program used to view PDF or PostScript files. Find that
under the "Programs" tab in the gretl Preferences dialog box (under the
Tools menu in the main window).

# 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: Model window, /Tests/Panel diagnostics

# heckit Estimation

Arguments: depvar indepvars ; selection equation
Options: --quiet (suppress printing of results)
--robust (QML standard errors)
--two-step (perform two-step estimation)
--vcv (print covariance matrix)
--verbose (print extra output)
Example: heckit y 0 x1 x2 ; ys 0 x3 x4
See also heckit.inp

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).

Please note that in ML estimation a numerical approximation of the Hessian
is used; this may lead to inaccuracies in the estimated covariance matrix if
the scale of the explanatory variables is such that some of the estimated
coefficients are very small in absolute value. This problem will be
addressed in future versions; in the meantime, rescaling the offending
explanatory variable(s) can be used as a workaround.

Menu path: /Model/Nonlinear models/Heckit

# help Utilities

Variants: help
help functions
help command
help function
Option: --func (select functions help)

If no arguments are given, prints a list of available commands. If the
single argument "functions" is given, prints a list of available functions
(see "genr").

help command describes command (e.g. help smpl). help function describes
function (e.g. help ldet). Some functions have the same names as related
commands (e.g. diff): in that case the default is to print help for the
command, but you can get help on the function by using the --func option.

Menu path: /Help

# hsk Estimation

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

This command is applicable where heteroskedasticity is present in the form
of an unknown function of the regressors which can be approximated by a
quadratic relationship. In that context it offers the possibility of
consistent standard errors and more efficient parameter estimates as
compared with OLS.

The procedure involves (a) OLS estimation of the model of interest, followed
by (b) an auxiliary regression to generate an estimate of the error
variance, then finally (c) weighted least squares, using as weight the
reciprocal of the estimated variance.

In the auxiliary regression (b) we regress the log of the squared residuals
from the first OLS on the original regressors and their squares. The log
transformation is performed to ensure that the estimated variances are
non-negative. Call the fitted values from this regression u^*. The weight
series for the final WLS is then formed as 1/exp(u^*).

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

# hurst Statistics

Argument: series

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: /Variable/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: filename
Examples: include myfile.inp
include sols.gfn

Intended for use in a command script, primarily for including definitions of
functions. Executes the commands in filename 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/Dataset info
Other access: Data browser windows

# intreg Estimation

Arguments: minvar maxvar indepvars
Options: --quiet (suppress printing of results)
--verbose (print details of iterations)
--robust (robust standard errors)
Example: intreg lo hi const x1 x2
See also wtp.inp

Estimates an interval regression model. This model arises when the dependent
variable is imperfectly observed for some (possibly all) observations. In
other words, the data generating process is assumed to be

y* = x b + u

but we only observe m <= y* <= M (the interval may be left- or
right-unbounded). Note that for some observations m may equal M. The
variables minvar and maxvar must contain NAs for left- and right-unbounded
observations, respectively.

The model is estimated by maximum likelihood, assuming normality of the
disturbance term.

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.

Menu path: /Model/Nonlinear models/Interval regression

# kalman Estimation

Options: --cross (allow for cross-correlated disturbances)
--diffuse (use diffuse initialization)

Opens a block of statements to set up a Kalman filter. This block should end
with the line end kalman, to which the options shown above may be appended.
The intervening lines specify the matrices that compose the filter. For
example,

kalman
obsy y
obsymat H
statemat F
statevar Q
end kalman

Please see the Gretl User's Guide for details.

See also "kfilter", "ksimul", "ksmooth".

# kpss Tests

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

For use of this command with panel data please see the final section in this
entry.

Computes the KPSS test (Kwiatkowski et al, Journal of Econometrics, 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.

The critical values shown for the test statistic are based on the response
surfaces estimated by Sephton (Economics Letters, 1995), which are more
accurate for small samples than the values given in the original KPSS
article. When the test statistic lies between the 10 percent and 1 percent
critical values a p-value is shown; this is obtained by linear interpolation
and should not be taken too literally.

Panel data

When the kpss command is used with panel data, to produce a panel unit root
test, the applicable options and the results shown are somewhat different.
While you may give a list of variables for testing in the regular
time-series case, with panel data only one variable may be tested per
command. And the --verbose option has a different meaning: it produces a
brief account of the test for each individual time series (the default being
to show only the overall result).

When possible, the overall test (null hypothesis: the series in question is
stationary for all the panel units) is calculated using the method of Choi
(Journal of International Money and Finance, 2001). This is not always
straightforward, the difficulty being that while the Choi test is based on
the p-values of the tests on the individual series, we do not currently have
a means of calculating p-values for the KPSS test statistic; we must rely on
a few critical values.

If the test statistic for a given series falls between the 10 percent and 1
percent critical values, we are able to interpolate a p-value. But if the
test falls short of the 10 percent value, or exceeds the 1 percent value, we
cannot interpolate and can at best place a bound on the global Choi test. If
the individual test statistic falls short of the 10 percent value for some
units but exceeds the 1 percent value for others, we cannot even compute a
bound for the global test.

Menu path: /Variable/Unit root tests/KPSS test

# labels Dataset

Variants: labels [ varlist ]
labels --to-file=filename
labels --from-file=filename

In the first form, prints out the informative labels (if present) for the
series in varlist, or for all series in the dataset if varlist is not
specified.

With the option --to-file, writes to the named file the labels for all
series in the dataset, one per line. If no labels are present an error is
flagged; if some series have labels and others do not, a blank line is
printed for series with no label.

With the option --from-file, reads the specified file (which should be plain
text) and assigns labels to the series in the dataset, reading one label per
line and taking blank lines to indicate blank labels.

Via the file writing and reading options it is possible to copy series
labels from one data file to another.

# 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 series which are lagged values of each of the series in varlist.
By default the number of lags created 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 series in varlist is
obtained and the result stored in a new series 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 discussions of studentized residuals and DFFITS see chapter 12 of
Maddala's Introduction to Econometrics or 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

# levinlin Tests

Arguments: order series
Options: --nc (test without a constant)
--ct (with constant and trend)
--quiet (suppress printing of results)
Examples: levinlin 0 y
levinlin 2 y --ct
levinlin {2,2,3,3,4,4} y

Carries out the panel unit-root test described by Levin, Lin and Chu (2002).
The null hypothesis is that all of the individual time series exhibit a unit
root, and the alternative is that none of the series has a unit root. (That
is, a common AR(1) coefficient is assumed, although in other respects the
statistical properties of the series are allowed to vary across
individuals.)

By default the test ADF regressions include a constant; to suppress the
constant use the --nc option, or to add a linear trend use the --ct option.
(See the "adf" command for explanation of ADF regressions.)

The (non-negative) order for the test (governing the number of lags of the
dependent variable to include in the ADF regressions) may be given in either
of two forms. If a scalar value is given, this is applied to all the
individuals in the panel. The alternative is to provide a matrix containing
a specific lag order for each individual; this must be a vector with as many
elements as there are individuals in the current sample range. Such a matrix
can be specified by name, or constructed using braces as illustrated in the
last example above.

Menu path: /Variable/Unit root tests/Levin-Lin-Chu test

# logistic Estimation

Arguments: depvar indepvars [ ymax=value ]
Option: --vcv (print covariance matrix)
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: depvar indepvars
Options: --robust (robust standard errors)
--multinomial (estimate multinomial logit)
--vcv (print covariance matrix)
--verbose (print details of iterations)
--p-values (show p-values instead of slopes)

If the dependent variable is a binary variable (all values are 0 or 1)
maximum likelihood estimates of the coefficients on indepvars 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. By default the slopes with
respect to each of the independent variables are calculated (at the means of
those variables) and these slopes replace the usual p-values in the
regression output. This behavior can be suppressed my giving the --p-values
option. The chi-square statistic tests the null hypothesis that all
coefficients are zero apart from the constant.

If the dependent variable is not binary but is discrete, then by default it
is interpreted as an ordinal response, and Ordered Logit estimates are
obtained. However, if the --multinomial option is given, the dependent
variable is interpreted as an unordered response, and Multinomial Logit
estimates are produced. (In either case, if the variable selected as
dependent is not discrete an error is flagged.) In the multinomial case, the
accessor $mnlprobs is available after estimation, to get a matrix containing
the estimated probabilities of the outcomes at each observation
(observations in rows, outcomes in columns).

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 chapter 12
of Ramanathan (2002).

Menu path: /Model/Nonlinear models/Logit

# logs Transformations

Argument: varlist

The natural log of each of the series in varlist is obtained and the result
stored in a new series 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 i=1991..2000
loop for (r=-.99; r<=.99; r+=.01)
loop foreach i xlist

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.

The parameter "control" may take any of five forms, as shown in the
examples: an integer number of times to repeat the commands within the loop;
"while" plus a boolean condition; a range of integer values for index
variable; "for" plus three expressions in parentheses, separated by
semicolons (which emulates the for statement in the C programming language);
or "foreach" plus an index variable and a list.

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: varlist
Options: --quiet (don't print anything)
--save (add distances to the dataset)
--vcv (print covariance matrix)

The Mahalanobis distance is the distance between two points in a
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

# makepkg Programming

Argument: filename
Options: --index (write auxiliary index file)
--translations (write auxiliary strings file)

Supports creation of a gretl function package via the command line. The
filename argument represents the name of the package to be created, and
should have the .gfn extension. Please see the Gretl User's Guide for
details.

The option flags support the writing of auxiliary files for use with gretl
"addons". The index file is a short XML document containing basic
information about the package; it has the same basename as the package and
the extension .xml. The translations file contains strings from the package
that may be suitable for translation, in C format; for package foo this file
is named foo-i18n.c.

Menu path: /File/Function files/New package

# 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: --quiet (don't show estimated model)
--vcv (print covariance matrix)
--hessian (base covariance matrix on the Hessian)
--robust (QML covariance matrix)
--verbose (print details of iterations)
--no-gradient-check (see below)
--lbfgs (use L-BFGS-B instead of regular BFGS)
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 and can be either scalars, or
vectors, or any combination of the two. 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).

If you supply analytical derivatives, by default gretl runs a numerical
check on their plausibility. Occasionally this may produce false positives,
instances where correct derivatives appear to be wrong and estimation is
refused. To counter this, or to achieve a little extra speed, you can give
the option --no-gradient-check. Obviously, you should do this only if you
are quite confident that the gradient you have specified is right.

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

# modprint Printing

Arguments: coeffmat names [ addstats ]

Prints the coefficient table and optional additional statistics for a model
estimated "by hand". Mainly useful for user-written functions.

The argument coeffmat should be a k by 2 matrix containing k coefficients
and k associated standard errors, and names should be a string containing at
least k names for the coefficients, separated by commas or spaces. (The
names argument may be either the name of a string variable or a literal
string, enclosed in double quotes.)

The optional argument addstats is a vector containing p additional
statistics to be printed under the coefficient table. If this argument is
given, then names should contain k + p comma-separated strings, the
additional p strings to be associated with the additional statistics.

# modtest Tests

Argument: [ order ]
Options: --normality (normality of residual)
--logs (non-linearity, logs)
--autocorr (serial correlation)
--arch (ARCH)
--squares (non-linearity, squares)
--white (heteroskedasticity, White's test)
--white-nocross (White's test, squares only)
--breusch-pagan (heteroskedasticity, Breusch-Pagan)
--robust (robust variance estimate for Breusch-Pagan)
--panel (heteroskedasticity, groupwise)
--comfac (common factor restriction, AR1 models only)
--quiet (don't print details)

Must immediately follow an estimation command. Depending on the option
given, this command carries out one of the following: the Doornik-Hansen
test for the normality of the error term; a Lagrange Multiplier test for
nonlinearity (logs or squares); White's test (with or without
cross-products) or the Breusch-Pagan test for heteroskedasticity; the LMF
test for serial correlation (Kiviet, 1986); a test for ARCH (Autoregressive
Conditional Heteroskedasticity; see also the "arch" command); or a test of
the common factor restriction implied by AR(1) estimation. With the
exception of the normality and common factor test most of the options are
only available for models estimated via OLS, but see below for details
regarding two-stage least squares.

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 --robust option applies only when the Breusch-Pagan test is selected;
its effect is to use the robust variance estimator proposed by Koenker
(1981), making the test less sensitive to the assumption of normality.

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).

The --comfac option is available only when the model is estimated via an
AR(1) method such as Hildreth-Lu. The auxiliary regression takes the form of
a relatively unrestricted dynamic model, which is used to test the common
factor restriction implicit in the AR(1) specification.

By default, the program prints the auxiliary regression on which the test
statistic is based, where applicable. This may be suppressed by using the
--quiet flag. The test statistic and its p-value may be retrieved using the
accessors $test and $pvalue respectively.

When a model has been estimated by two-stage least squares (see "tsls"), the
LM principle breaks down and gretl offers some equivalents: the --autocorr
option computes Godfrey's test for autocorrelation (Godfrey, 1994) while the
--white option yields the HET1 heteroskedasticity test (Pesaran and Taylor,
1999).

Menu path: Model window, /Tests

# mpols Estimation

Arguments: depvar indepvars
Options: --vcv (print covariance matrix)
--simple-print (do not print auxiliary statistics)
--quiet (suppress printing of results)

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 indepvars 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 indepvars. 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

# negbin Estimation

Arguments: depvar indepvars [ ; offset ]
Options: --model1 (use NegBin 1 model)
--opg (see below)
--robust (QML covariance matrix)
--vcv (print covariance matrix)
--verbose (print details of iterations)

Estimates a Negative Binomial model. The dependent variable is taken to
represent a count of the occurrence of events of some sort, and must have
only non-negative integer values. By default the model NegBin 2 is used, in
which the conditional variance of the count is given by mu(1 + αmu), where
mu denotes the conditional mean. But if the --model1 option is given the
conditional variance is mu(1 + α).

The optional offset series works in the same way as for the "poisson"
command. The Poisson model is a restricted form of the Negative Binomial in
which α = 0 by construction.

Menu path: /Model/Nonlinear models/Count data...

# nls Estimation

Arguments: function [ derivatives ]
Options: --quiet (don't show estimated model)
--robust (robust standard errors)
--vcv (print covariance matrix)
--verbose (print details of iterations)
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. Any
options should be appended to this line.

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, the default)
--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
Option: --preserve (preserve matrices)
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.

By default, this command cleans out all data in gretl's current workspace.
If you give the --preserve option, however, any currently defined matrices
are retained.

Menu path: /File/New data set

# ols Estimation

Arguments: depvar indepvars
Options: --vcv (print covariance matrix)
--robust (robust standard errors)
--jackknife (see below)
--simple-print (do not print auxiliary statistics)
--quiet (suppress printing of results)
--anova (print an ANOVA table)
--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 indepvars 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 directly after estimation. For example

genr uh = $uhat

saves the residuals under the name uh. See the "accessors" section of the
gretl function reference for details.

The specific formula used for generating robust standard errors (when the
--robust option is given) can be adjusted via the "set" command. The
--jackknife option has the effect of selecting an hc_version of 3a; it is
provided to emulate the old hccm command.

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

# omit Tests

Argument: varlist
Options: --test-only (don't replace the current model)
--chi-square (give chi-square form of Wald test)
--quiet (print only the basic test result)
--silent (don't print anything)
--vcv (print covariance matrix for reduced model)
--auto[=alpha] (sequential elimination, see below)
Examples: omit 5 7 9
omit seasonals --quiet
omit --auto
omit --auto=0.05

This command must follow an estimation command. It calculates a Wald test
for the joint significance of the variables in varlist, which should be a
subset of the independent variables in the model last estimated. The results
of the test may be retrieved using the accessors $test and $pvalue.

By default the restricted model is estimated and it replaces the original as
the "current model" for the purposes of, for example, retrieving the
residuals as $uhat or doing further tests. This behavior may be suppressed
via the --test-only option.

By default the F-form of the Wald test is recorded; the --chi-square option
may be used to record the chi-square form instead.

If the restricted model is both estimated and printed, the --vcv option has
the effect of printing its covariance matrix, otherwise this option is
ignored.

Alternatively, if the --auto flag is given, sequential elimination is
performed: at each step the variable with the highest p-value is omitted,
until all remaining variables have a p-value no greater than some cutoff.
The default cutoff is 10 percent (two-sided); this can be adjusted by
appending "=" and a value between 0 and 1 (with no spaces), as in the fourth
example above. If varlist is given this process is confined to the listed
variables, otherwise all variables are treated as candidates for omission.
Note that the --auto and --test-only options cannot be combined.

Menu path: Model window, /Tests/Omit variables

# open Dataset

Argument: filename
Options: --quiet (don't print list of series)
--preserve (preserve any matrices and scalars)
--www (use a database on the gretl server)
See below for additional specialized options
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. To add data to the current dataset, see "append".

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, SPSS,
etc.).

If the filename argument takes the form of a URI starting with http://, then
gretl will attempt to download the indicated data file before opening it.

By default, opening a new data file clears the current gretl session, which
includes deletion of any named matrices and scalars. If you wish to keep any
currently defined matrices and scalars, use the --preserve option.

The open command can also be used to open a database (gretl, RATS 4.0 or
PcGive) 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.

When opening a spreadsheet file (Gnumeric, Open Document or XLS), you may
give up to three additional parameters following the filename. First, you
can select a particular worksheet within the file. This is done either by
giving its (1-based) number, using the syntax, e.g., --sheet=2, or, if you
know the name of the sheet, by giving the name in double quotes, as in
--sheet="MacroData". The default is to read the first worksheet. You can
also specify a column and/or row offset into the worksheet via, e.g.,

--coloffset=3 --rowoffset=2

which would cause gretl to ignore the first 3 columns and the first 2 rows.
The default is an offset of 0 in both dimensions, that is, to start reading
at the top-left cell.

With plain text files, gretl generally expects to find the data columns
delimited in some standard manner. But there is also a special facility for
reading "fixed format" files, in which there are no delimiters but there is
a known specification of the form, e.g., "variable k occupies 8 columns
starting at column 24". To read such files, you should append a string
--cols=colspec, where colspec is composed of comma-separated integers. These
integers are interpreted as a set of pairs. The first element of each pair
denotes a starting column, measured in bytes from the beginning of the line
with 1 indicating the first byte; and the second element indicates how many
bytes should be read for the given field. So, for example, if you say

open fixed.txt --cols=1,6,20,3

then for variable 1 gretl will read 6 bytes starting at column 1; and for
variable 2, 3 bytes starting at column 20. Lines that are blank, or that
begin with #, are ignored, but otherwise the column-reading template is
applied, and if anything other than a valid numerical value is found an
error is flagged. If the data are read successfully, the variables will be
named v1, v2, etc. It's up to the user to provide meaningful names and/or
descriptions using the commands "rename" and/or "setinfo".

Menu path: /File/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.

Three special variants on the above are available. If you give the keyword
null in place of a real filename along with the --write option, the effect
is to suppress all printed output until the next outfile --close. In
addition if the keywords stdout or stderr are given in place of a regular
filename the effect is to redirect output to standard output or standard
error output respectively.

# panel Estimation

Arguments: depvar indepvars
Options: --vcv (print covariance matrix)
--fixed-effects (estimate with group fixed effects)
--random-effects (random effects or GLS model)
--between (estimate the between-groups model)
--robust (robust standard errors; see below)
--time-dummies (include time dummy variables)
--unit-weights (weighted least squares)
--iterate (iterative estimation)
--matrix-diff (use matrix-difference method for Hausman test)
--quiet (less verbose output)
--verbose (more verbose output)

Estimates a panel model. By default the fixed effects estimator is used;
this is implemented 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. In this case (only) the
option --matrix-diff forces use of the matrix-difference method (as opposed
to the regression method) for carrying out the Hausman test for the
consistency of the random effects estimator.

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).

The --robust option is available only for fixed effects models. The default
variant is the Arellano HAC estimator, but Beck-Katz "Panel Corrected
Standard Errors" can be selected via the command set pcse on.

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

Menu path: /Model/Panel

# pca Statistics

Argument: varlist
Options: --covariance (use the covariance matrix)
--save (save major components)
--save-all (save all components)

Principal Components Analysis. Prints the eigenvalues of the correlation
matrix (or the covariance matrix if the --covariance option is given) for
the variables in varlist, 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 the
mean (which means greater than 1.0 if the analysis is based on the
correlation matrix) are saved to the dataset as series, 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
varlist.

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: series [ bandwidth ]
Options: --bartlett (use Bartlett lag window)
--log (use log scale)
--radians (show frequency in radians)
--degrees (show frequency in degrees)

Computes and displays (and if not in batch mode, graphs) the spectrum of the
specified series. By default the sample periodogram is given, but optionally
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.

The (mutually exclusive) options --radians and --degrees influence the
appearance of the frequency axis when the periodogram is graphed. By default
the frequency is scaled by the number of periods in the sample, but these
options cause the axis to be labeled from 0 to pi radians or from 0 to
180degrees, respectively.

Menu path: /Variable/Periodogram
Other access: Main window pop-up menu (single selection)

# poisson Estimation

Arguments: depvar indepvars [ ; offset ]
Options: --robust (robust standard errors)
--vcv (print covariance matrix)
--verbose (print details of iterations)
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.

gretl-common 1.9.6-1build1 / usr / share / gretl / gretlcli.hlp.pt