package com.imsl.test.example.stat;

import com.imsl.math.*;
import com.imsl.stat.*;

/**
 * <p>Fits a vector autoregression to a time series.
 * </p>
 * <p>
 * This example fits a \(\text{VAR}(1)\) to a \(2\)-dimensional vector time series. Then,
 * forecasts for up to \(4\) steps ahead are requested, illustrating the default
 * forecasting behavior.</p>
 *
 *
 * @see <a href="VectorAutoregressionEx1.java">Code</a>
 * @see <a href="doc-files/VectorAutoregressionEx1.out">Output</a>
 */

public class VectorAutoregressionEx1 {

    public static void main(String args[]) {
        double data1[] = {
            0, 0,
            -0.148105526820657, 0.0507420782620461,
            -1.13674727366735, 0.862892721126079,
            1.54366183541037, -1.13804802266371,
            -0.0923211737957721, 1.65649055346038,
            0.521764564424907, -2.11208211206815,
            0.843683397890515, 2.56656798707681,
            -2.70330819114831, -2.83452914666041,
            4.93683704766295, 3.95965377457266,
            -4.78314880243807, -2.23136673998374,
            6.24911547448071, 0.40543051970714,
            -6.76582567372196, 0.816818897274206,
            6.21142944093012, -4.247694573354,
            -5.29817270483491, 5.08246614505046,
            4.19557583927549, -5.35697380907112,
            -3.21572784971078, 7.89716072829777,
            0.485717553966479, -8.25930665413043,
            2.69490292018773, 10.9017252520684,
            -5.41090143363388, -10.400477539227,
            8.29423327234419, 9.10321370415527
        };

        TimeSeries ts = new TimeSeries();
        // Set the data for the two-dimensional time series
        ts.setSeriesValues(data1, 2);

        // Set up the vector autoregression model. Use default AR lag = 1.
        VectorAutoregression VAR1 = new VectorAutoregression(ts);

        // Get estimates of coefficient matrix A1
        double[][] coefs = VAR1.getEstimates();

        // Print estimates 
        PrintMatrix pm = new PrintMatrix("Estimated Coefficient Matrix A1");
        pm.print(coefs);

        // Get h =1, 2, 3, 4 step ahead forecasts for all time points past
        // the default presample value (nT = 6)
        double[][] forecasts = VAR1.getForecasts();

        // Print forecasts
        pm = new PrintMatrix("VAR Forecasts");
        PrintMatrixFormat pmf = new PrintMatrixFormat();
        String[] colLabels = {"(h=1,k=1)", "(h=1,k=2)", "(2,1)",
            "(2,2)", "(3,1)", "(3,2)", "(4,1)", "(4,2)"};
        pmf.setColumnLabels(colLabels);
        pmf.setNoRowLabels();
        pm.print(pmf, forecasts);
    }
}