lsq ()

MODEL
lsq (const int *list,
     DATASET *dset,
     GretlCmdIndex ci,
     gretlopt opt);

Computes least squares estimates of the model specified by list , using an estimator determined by the value of ci .

ar_model ()

MODEL
ar_model (const int *list,
          DATASET *dset,
          gretlopt opt,
          PRN *prn);

Estimate the model given in list using the generalized Cochrane-Orcutt procedure for autoregressive errors.

ar1_model ()

MODEL
ar1_model (const int *list,
           DATASET *dset,
           gretlopt opt,
           PRN *prn);

lad_model ()

MODEL
lad_model (const int *list,
           DATASET *dset,
           gretlopt opt);

Estimate the model given in list using the method of Least Absolute Deviation (LAD).

quantreg ()

MODEL
quantreg (const gretl_matrix *tau,
          const int *list,
          DATASET *dset,
          gretlopt opt,
          PRN *prn);

Estimate the model given in list using the method of quantile regression.

arma ()

MODEL
arma (const int *list,
      const int *pqlags,
      DATASET *dset,
      gretlopt opt,
      PRN *prn);

Calculates ARMA estimates. By default the estimator is exact ML, implemented via gretl's Kalman filter in conjunction with the BFGS maximizer. Other options: if opt includes OPT_L we use L-BFGS-B rather than standard BFGS; OPT_C (incompatible with OPT_L) calls for use of conditional ML (BHHH algorithm); and OPT_X requests estimation via X-12-ARIMA rather than native code.

If pqlags is non-NULL it should take the form of two gretl sub-lists joined by LISTSEP: the first sub-list holds a set of AR lags and the second a list of MA lags. If only AR lags are specified, a single list may be given; if only MA lags are specified, use LISTSEP with a null first sub-list.

If the model specification does not include a constant this is added automatically, unless opt includes OPT_N ("no constant").

When the estimation method is native exact ML, two (mutually exclusive) flags in opt may be used to control the estimator of the covariance matrix: OPT_G specifies use of the outer product of the gradient (OPG), while OPT_H specifies use of the (numerical) Hessian. If neither of these flags is given, the default is to use the Hessian by preference, but to fall back to OPG if computation of the numerical Hessian fails. These flags are ignored if estimation is not via native exact ML.

garch ()

MODEL
garch (const int *list,
       DATASET *dset,
       gretlopt opt,
       PRN *prn);

Calculate GARCH estimates.

mp_ols ()

MODEL
mp_ols (const int *list,
        DATASET *dset,
        gretlopt opt);

Estimate an OLS model using multiple-precision arithmetic via the GMP library.

panel_model ()

MODEL
panel_model (const int *list,
             DATASET *dset,
             gretlopt opt,
             PRN *prn);

Calculate estimates for a panel dataset, using fixed effects (the default), random effects, or weighted least squares based on the respective variances for the cross-sectional units.

ivreg ()

MODEL
ivreg (const int *list,
       DATASET *dset,
       gretlopt opt);

Calculate IV estimates for a linear model, by default via two-stage least squares. The option flags can include OPT_Q for quiet operation, and either OPT_L to specify estimation via Limited Information Maximum Likelihood or OPT_G for estimation via Generalized method of Moments.

dpd_model ()

MODEL
dpd_model (const int *list,
           const int *laglist,
           const char *ispec,
           const DATASET *dset,
           gretlopt opt,
           PRN *prn);

Implements the "dpanel" command.

hsk_model ()

MODEL
hsk_model (const int *list,
           DATASET *dset,
           gretlopt opt);

Estimate the model given in list using a correction for heteroskedasticity.

arch_model ()

MODEL
arch_model (const int *list,
            int order,
            DATASET *dset,
            gretlopt opt);

Estimate the model given in list via weighted least squares, with the weights based on the predicted error variances from an auxiliary regression of the squared residuals on their lagged values.

whites_test ()

int
whites_test (MODEL *pmod,
             DATASET *dset,
             gretlopt opt,
             PRN *prn);

Runs White's test for heteroskedasticity on the given model.

arch_test ()

int
arch_test (MODEL *pmod,
           int order,
           const DATASET *dset,
           gretlopt opt,
           PRN *prn);

Tests pmod for AutoRegressive Conditional Heteroskedasticity.

array_arch_test ()

int
array_arch_test (const double *u,
                 int n,
                 int order,
                 gretlopt opt,
                 PRN *prn);

makevcv ()

int
makevcv (MODEL *pmod,
         double sigma);

Inverts the Cholesky-decomposed X'X and computes the coefficient covariance matrix.

augment_regression_list ()

int *
augment_regression_list (const int *orig,
                         int aux,
                         DATASET *dset,
                         int *err);

Augment the regression list orig with auxiliary terms. If aux is AUX_SQ add the squares of the original regressors; if aux is AUX_WHITE add squares and cross-products, or if aux is AUX_LOG add the natural logs of the original regressors. If they are not already present, these variables are added to the data array.

anova ()

int
anova (const int *list,
       const DATASET *dset,
       gretlopt opt,
       PRN *prn);

Does one-way or two-way Analysis of Variance (prints table and F-test).