root/library/tests/arx_test.cpp @ 679

/*!
\file
\brief Test of basic elements of the ARX class

See file \ref arx for mathematical background.

This class tests functions ARX::bayes (Bayes rule) ARX::structure_est and ARX::predictor_student

Untested functions: none.

 */

#include "estim/arx.h"
using namespace bdm;

int main() {
        // Setup model
        vec th ( "0.8 -0.3 0.4 0.01" );
        int ord = th.length(); //auxiliary variable
        double sqr = 0.1;

        //Test constructor
        mat V0 = 0.00001 * eye ( ord + 1 );
        V0 ( 0.0 ) = 1; //
        double nu0 = ord + 5.0;

        ARX Ar;
        Ar.set_statistics ( 1, V0, nu0 );               // Estimator
        Ar.set_constant(false);
        Ar.validate();
        const epdf& f_thr = Ar.posterior();          // refrence to posterior of the estimator

        //Test estimation
        int ndat = 100;                 // number of data records
        vec Yt ( ndat );                // Store generated data
        Yt.set_subvector ( 0, randn ( ord ) ); //initial values
        vec rgr ( ord );                // regressor

        //print moments of the prior distribution
        cout << "prior mean: " << f_thr.mean() << endl;
        cout << "prior variance: " << f_thr.variance() << endl;

        // cycle over time:
        for ( int t = ord; t < ndat; t++ ) {
                //Generate regressor
                for ( int j = 0; j < ( ord ); j++ ) {
                        rgr ( j ) = Yt ( t - j - 1 );
                }
                //model
                Yt ( t ) = th * rgr + sqr * NorRNG();

                Ar.bayes ( vec_1(Yt(t)), rgr );             // Bayes rule

                // Build predictor
                mlstudent*      Pr = Ar.predictor_student ( );
                // Test similarity of likelihoods from the Bayes rule and the predictor
                cout << "BR log-lik: " << Ar._ll();
                cout << " , predictor ll: " <<  Pr->evallogcond ( vec_1 ( Yt ( t ) ), rgr )  << endl;
                delete Pr;
        }
        //print posterior moments
        cout << "posterior mean: " << f_thr.mean() << endl;
        cout << "posterior variance: " << f_thr.variance() << endl;

        // Test brute-froce structure estimation

        cout << "Structure estimation: " << endl;
        cout << Ar.structure_est ( egiw ( 1, V0, nu0 ) ) << endl;
}