Nonlinear models

Nonlinear models — estimation of nonlinear models

Functions

Types and Values

typedef nlspec

Object Hierarchy


Includes

#include <libgretl.h>

Description

Provides mechanisms for estimating nonlinear models via Nonlinear Least Squares, Maximum Likelihood, or GMM.

Functions

nlspec_new ()

nlspec *
nlspec_new (int ci,
            const DATASET *dset);

Parameters

ci

NLS, MLE or GMM.

 

dset

information on dataset.

 

Returns

a pointer to a newly allocated nonlinear model specification, or NULL on failure.


nlspec_destroy ()

void
nlspec_destroy (nlspec *spec);

Frees all resources associated with spec , and frees the pointer itself.

Parameters

spec

pointer to nls specification.

 

nlspec_add_param_with_deriv ()

int
nlspec_add_param_with_deriv (nlspec *spec,
                             const char *s);

Adds an analytical derivative to spec . This pointer must have previously been obtained by a call to nlspec_new(). The required format for dstr is "varname = formula", where varname is the name of the (scalar) variable holding the parameter in question, and formula is an expression, of the sort that is fed to gretl's genr command, giving the derivative of the regression function in spec with respect to the parameter. The variable holding the parameter must be already present in the dataset.

Parameters

spec

pointer to nls specification.

 

s

string specifying a derivative with respect to a parameter of the regression function.

 

Returns

0 on success, non-zero error code on error.


nlspec_add_param_list ()

int
nlspec_add_param_list (nlspec *spec,
                       int np,
                       double *vals,
                       char **names);

Adds to spec a list of (scalar) parameters to be estimated. For an example of use see nls_example.c in the gretl extra subdirectory.

Parameters

spec

nls specification.

 

np

number of parameters.

 

vals

array of initial parameter values.

 

names

array of parameter names.

 

Returns

0 on success, non-zero error code on error.


aux_nlspec_add_param_list ()

int
aux_nlspec_add_param_list (nlspec *spec,
                           int np,
                           double *vals,
                           char **names);

Returns


nlspec_set_regression_function ()

int
nlspec_set_regression_function (nlspec *spec,
                                const char *fnstr,
                                const DATASET *dset);

Adds the regression function to spec . This pointer must have previously been obtained by a call to nlspec_new(). The required format for fnstr is "varname = formula", where varname is the name of the dependent variable and formula is an expression of the sort that is fed to gretl's genr command. The dependent variable must be already present in the dataset.

Parameters

spec

pointer to nls specification.

 

fnstr

string specifying nonlinear regression function.

 

dset

information on dataset.

 

Returns

0 on success, non-zero error code on error.


nlspec_set_t1_t2 ()

void
nlspec_set_t1_t2 (nlspec *spec,
                  int t1,
                  int t2);

Sets the sample range for estimation of spec . This pointer must have previously been obtained by a call to nlspec_new().

Parameters

spec

pointer to nls specification.

 

t1

starting observation.

 

t2

ending observation.

 

nl_parse_line ()

int
nl_parse_line (int ci,
               const char *line,
               const DATASET *dset,
               PRN *prn);

This function is used to create the specification of a nonlinear model, to be estimated via nl_model (i.e. via NLS, MLE or GMM). It can be called several times to build up the required information. See the manual entries for nls, mle and gmm for details.

Parameters

ci

NLS, MLE or GMM.

 

line

string containing information to be added to a nonlinear model specification.

 

dset

dataset struct.

 

prn

gretl printing struct (for warning messages).

 

Returns

0 on success, non-zero error code on failure.


nl_model ()

MODEL
nl_model (DATASET *dset,
          gretlopt opt,
          PRN *prn);

Computes estimates of a model via nonlinear least squares, maximum likelihood, or GMM. The model must have been specified previously, via calls to the function nl_parse_line. Those calls determine, among other things, which estimator will be used.

Parameters

dset

dataset struct.

 

opt

may include OPT_V for verbose output, OPT_R for robust covariance matrix.

 

prn

printing struct.

 

Returns

a model struct containing the parameter estimates and associated statistics.


model_from_nlspec ()

MODEL
model_from_nlspec (nlspec *spec,
                   DATASET *dset,
                   gretlopt opt,
                   PRN *prn);

Computes estimates of the model specified in spec . The spec must first be obtained using nlspec_new(), and initialized using nlspec_set_regression_function(). If analytical derivatives are to be used (which is optional but recommended) these are set using nlspec_add_param_with_deriv().

Parameters

spec

nonlinear model specification.

 

dset

dataset struct.

 

opt

may include OPT_V for verbose output, OPT_A to treat as an auxiliary model, OPT_C to produce coefficient estimates only (don't bother with GNR to produce standard errors).

 

prn

printing struct.

 

Returns

a model struct containing the parameter estimates and associated statistics.


nls_boot_calc ()

int
nls_boot_calc (const MODEL *pmod,
               DATASET *dset,
               int ft1,
               int ft2,
               double *fcerr);

Returns


get_default_nls_toler ()

double
get_default_nls_toler (void);

Returns

the default value used in the convergence criterion for estimation of models using nonlinear least squares.

Types and Values

nlspec

typedef struct nlspec_ nlspec;

An opaque structure handled only via accessor functions.