ISmRedundantVariablesTest.Explained

Syntax

Explained: ISlSerie;

Description

The Explained property determines an explained series.

Comments

Use the ISmRedundantVariablesTest.Explanatories property to set explanatory series.

Example

Add a link to the Stat system assembly.

Sub UserProc;
Var
    rtest: SmRedundantVariablesTest;
    d0: Double;
    y, x, y1, y2: Array[43] Of Double;
    ARMA: ISlARMA;
    OrderList, redlist: Array[ 1] Of  Integer;
    res, i: Integer;
Begin

    // Set values fornbsp;variables
    y[00] := 6209; x[00] := 4110; y1[00] := 3415; y2[00] := 2822;
    y[01] := 6385; x[01] := 4280; y1[01] := 3673; y2[01] := 3023;
    y[02] := 6752; x[02] := 4459; y1[02] := 4013; y2[02] := 3131;
    y[03] := 6837; x[03] := 4545; y1[03] := 4278; y2[03] := 3351;
    y[ 04] := 6495; x[04] := 4664; y1[04] := 4577; y2[04] := 3463 ;
    y[ 05] := 6907; x[05] := 4861; y1[05] := 5135; y2[05] := 3686 ;
    y[ 06] := 7349; x[06] := 5195; y1[06] := 5388; y2[06] := 3815 ;
    y[07] := 7213; x[07] := 5389; y1[07] := 5610; y2[07] := 3960;
    y[ 08] := 7061; x[08] := 5463; y1[08] := 5787; y2[08] := 4119 ;
    y[09] := 7180; x[09] := 5610; y1[09] := 6181; y2[09] := 4351;
    y[10] := 7132; x[10] := 5948; y1[10] := 6633; y2[10] := 4641;
    y[ 11] := 7137; x[11] := 6218; y1[11] := 6910; y2[11] := 5008 ;
    y[ 12] := 7473; x[12] := 6521; y1[12] := 7146; y2[12] := 5305 ;
    y[ 13] := 7722; x[13] := 6788; y1[13] := 7248; y2[13] := 5611 ;
    y[ 14] := 8088; x[14] := 7222; y1[14] := 7689; y2[14] := 5693 ;
    y[ 15] := 8516; x[15] := 7486; y1[15] := 8046; y2[15] := 5804 ;
    y[ 16] := 8941; x[16] := 7832; y1[16] := 8143; y2[16] := 6121 ;
    y[ 17] := 9064; x[17] := 8153; y1[17] := 8064; y2[17] := 6546 ;
    y[ 18] := 9380; x[18] := 8468; y1[18] := 8556; y2[18] := 6918 ;
    y[ 19] := Double.Nan; x[19] := 9054; y1[19] := 9177; y2[19] := 7349 ;
    y[ 20] := 9907; x[20] := 9499; y1[20] := 9705; y2[20] := 7769 ;
    y[ 21] := 10333; x[21] := 9866; y1[21] := 9923; y2[21] := 7809 ;
    y[22] := 10863; x[22] := 10217; y1[22] := 10268; y2[22] := 7951;
    y[ 23] := 11693; x[23] := 10763; y1[23] := 10681; y2[23] := 8395 ;
    y[ 24] := 12242; x[24] := 10683; y1[24] := 10448; y2[24] := 8653 ;
    y[ 25] := 12227; x[25] := 10494; y1[25] := 10366; y2[25] := 8304 ;
    y[ 26] := 12910; x[26] := 10938; y1[26] := 10958; y2[26] := 8809 ;
    y[ 27] := 13049; x[27] := 11198; y1[27] := 11292; y2[27] := 9028 ;
    y[28] := 13384; x[28] := 11546; y1[28] := 11726; y2[28] := 9314;
    y[ 29] := 14036; x[29] := 11865; y1[29] := 12172; y2[29] := 9820 ;
    y[ 30] := Double.Nan; x[30] := 11781; y1[30] := 12058; y2[30] := 10246 ;
    y[ 31] := 14704; x[31] := 11681; y1[31] := 11804; y2[31] := 10153 ;
    y[ 32] := 13802; x[32] := 11903; y1[32] := 11682; y2[32] := 10197 ;
    y[ 33] := 14197; x[33] := 11900; y1[33] := 12001; y2[33] := 10294 ;
    y[ 34] := 15010; x[34] := 11986; y1[34] := 12300; y2[34] := 10555 ;
    y[ 35] := 15589; x[35] := 12206; y1[35] := 12535; y2[35] := 10808 ;
    y[ 36] := 15932; x[36] := 12734; y1[36] := 13173; y2[36] := 11318 ;
    y[ 37] := 16631; x[37] := 12990; y1[37] := 13482; y2[37] := 11683 ;
    y[ 38] := 17394; x[38] := 13516; y1[38] := 13945; y2[38] := 12153 ;
    y[ 39] := 17758; x[39] := 13866; y1[39] := 14278; y2[39] := 12464 ;
    y[ 40] := 17308; x[40] := 14141; y1[40] := 14840; y2[40] := 12782 ;
    y[ 41] := Double.Nan; x[41] := 14141; y1[41] := 15263; y2[41] := 13066 ;
    y[42] := 16413; x[42] := 14237; y1[42] := 15357; y2[42] := 13113;

    rtest := New SmRedundantVariablesTest.Create;
     // Set explained and explanatory variables
    rtest.Explained.Value := y;
    rtest.Explanatories.Add.Value := x;
    rtest.Explanatories.Add.Value := y1;
    rtest.Explanatories.Add.Value := y2;
     // Set method of constant determination
    rtest.ModelCoefficients.Intercept.Mode := InterceptMode.AutoEstimate;
     // Set calculation periods
    rtest.ModelPeriod.FirstPoint := 1 ;
    rtest.ModelPeriod.LastPoint :=  43 ;
     // Set missing data treatment method
    rtest.MissingData.Method := MissingDataMethod.LinTrend;
     // Set moving average parameters
    ARMA := rtest.ARMA;
    OrderList[ 0] := 1 ;
    ARMA.OrderMA := OrderList;
    ARMA.CalcInitMode := ARMAInitType.Auto;
     // Set redundant regressors
    redlist[0] := 0 ;
    rtest.RedundantExplanatories := redlist;

    // Execute calculation and display results
    res := rtest.Execute;
     If res <> 0 Then
        Debug.WriteLine(rtest.Errors);
    Else
        Debug.Indent;
        Debug.WriteLine("Fisher test");
        Debug.Unindent;
        d0 := rtest.FTest.Statistic;
        Debug.WriteLine("value: " + d0.ToString);
        d0 := rtest.FTest.Probability;
        Debug.WriteLine("probability: " + d0.ToString);
        Debug.Indent;
        Debug.WriteLine("Chi-square test");
        Debug.Unindent;
        d0 := rtest.ChiTest.Statistic;
        Debug.WriteLine("value: " + d0.ToString);
        d0 := rtest.ChiTest.Probability;
        Debug.WriteLine("probability: " + d0.ToString);
        Debug.Indent;
        Debug.WriteLine("Modeling series");
        Debug.Unindent;
        For i := 0 To rtest.Fitted.Length - 1 Do
            Debug.Write(i.ToString +  ", " );
            Debug.WriteLine(rtest.Fitted[i]);
        End For;
        Debug.Indent;
        Debug.WriteLine("Residuals");
        Debug.Unindent;
        For i := 0 To rtest.Residuals.Length - 1 Do
            Debug.Write(i.ToString + ", ");
            Debug.WriteLine(rtest.Residuals[i]);
        End For;
        Debug.Indent;
        Debug.WriteLine("Summary statistics");
        Debug.Unindent;
        d0 := rtest.SummaryStatistics.AIC;
        Debug.WriteLine("Akaike criterion: " + d0.ToString);
        d0 := rtest.SummaryStatistics.DW;
        Debug.WriteLine("Durbin-Watson statistic: " + d0.ToString);
    End If;
End Sub UserProc;

After executing the example the console window displays Fisher test and chi-square test calculation results, the modeling series, the residual series, and summary statistics.