root/tests/arx_test.cpp @ 265

/*!
\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;

        RV thr ( "{theta_and_r }",vec_1 ( ord+1 ) );   // Descriptive random variable
        ARX Ar ( thr, V0, nu0 );                 // Estimator
        const epdf& f_thr = Ar._epdf();          // 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
        vec Psi ( ord+1 );              // extended 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();

                Psi = concat ( Yt ( t ), rgr ); // Inefficient! Used for compatibility with Matlab!
                Ar.bayes ( Psi );             // Bayes rule

                // Build predictor
                mlstudent*      Pr = Ar.predictor_student ( RV ( "{y }" ),RV ( "{y1 y2 y3 y4 }" ) );
                // 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 ( thr,V0,nu0 ) ) <<endl;
}