ISmRollingRegression.CoefficientsMatrix

Syntax

CoefficientsMatrix: Array;

Description

The CoefficientsMatrix returns matrix of coefficients estimates.

Comments

To get estimate characteristics, use the ISmRollingRegression.StandartErrorMatrix, ISmRollingRegression.TStatisticMatrix, ISmRollingRegression.PValueMatrix properties.

Example

To execute the example, add a link to the Stat system assembly.

Sub UserProc;
Var
    RR: SmRollingRegression;
    Explanatories: ISlSerie;
    can, fra, ger: Array[10] Of Double;
    i, j, res, rows, cols: Integer;
Begin
    RR := New SmRollingRegression.Create;
    Can[0] := 6209; fra[0] := 4110; ger[0] := 3415;
    Can[1] := 6385; fra[1] := 4280; ger[1] := 3673;
    Can[2] := double.Nan; fra[2] := 4459; ger[2] := 4013;
    Can[3] := 6837; fra[3] := 4545; ger[3] := 4278;
    Can[4] := 6495; fra[4] := 4664; ger[4] := 4577;
    Can[5] := 6907; fra[5] := 4861; ger[5] := 5135;
    Can[6] := 7349; fra[6] := 5195; ger[6] := 5388;
    Can[7] := 7213; fra[7] := 5389; ger[7] := 5610;
    Can[8] := 7061; fra[8] := 5463; ger[8] := 5787;
    Can[9] := 7180; fra[9] := 5610; ger[9] := 6181;
    //explained series
    RR.Explained.Value := can;
    // explanatory series
    RR.Explanatories.Clear;
    Explanatories := RR.Explanatories.Add;
    Explanatories.Value := fra;
    Explanatories := RR.Explanatories.Add;
    Explanatories.Value := ger;
    // sample period
    RR.ModelPeriod.FirstPoint := 1;
    RR.ModelPeriod.LastPoint := 10;
    // method of missing data treatment - linear trend
    RR.MissingData.Method := MissingDataMethod.LinTrend;
    // window shift interval
    RR.RollingStep := 1;
    // the width of rolling regression window
    RR.Window := 4;
    // model constant - constant is estimated automatically
    RR.Intercept.Mode := InterceptMode.AutoEstimate;
    res := RR.Execute;
    If res = 0 Then
        For i := 0 To RR.WarningsCount - 1 Do
            Debug.WriteLine(RR.Warnings[i])
        End For;
        Debug.WriteLine("==Coefficients==");
        rows := RR.CoefficientsMatrix.GetUpperBound(1);
        cols := RR.CoefficientsMatrix.GetUpperBound(2);
        Debug.Indent;
        For i := 0 To rows Do
            For j := 0 To cols Do
                Debug.Write(RR.CoefficientsMatrix[i, j]);
            End For;
            Debug.WriteLine(" ");
        End For;
        Debug.Unindent;
        Debug.WriteLine("==Standard errors==");
        rows := RR.StandartErrorMatrix.GetUpperBound(1);
        cols := RR.StandartErrorMatrix.GetUpperBound(2);
        Debug.Indent;
        For i := 0 To rows Do
            For j := 0 To cols Do
                Debug.Write(RR.StandartErrorMatrix[i, j]);
            End For;
            Debug.WriteLine(" ");
        End For;
        Debug.Unindent;
        Debug.WriteLine("==Statistics==");
        rows := RR.TStatisticMatrix.GetUpperBound(1);
        cols := RR.TStatisticMatrix.GetUpperBound(2);
        Debug.Indent;
        For i := 0 To rows Do
            For j := 0 To cols Do
                Debug.Write(RR.TStatisticMatrix[i, j]);
            End For;
            Debug.WriteLine(" ");
        End For;
        Debug.Unindent;
        Debug.WriteLine("==Probability==");
        rows := RR.PValueMatrix.GetUpperBound(1);
        cols := RR.PValueMatrix.GetUpperBound(2);
        Debug.Indent;
        For i := 0 To rows Do
            For j := 0 To cols Do
                Debug.Write(RR.PValueMatrix[i, j]);
            End For;
            Debug.WriteLine(" ");
        End For;
        Debug.Unindent;
        Debug.WriteLine("==Forecast series==");
        Debug.Indent;
        For i := 0 To RR.RollingForecast.Length - 1 Do
            Debug.Write(i.ToString + " ");
            Debug.WriteLine(RR.RollingForecast[i]);
        End For;
        Debug.Unindent;
        Else
            Debug.WriteLine(RR.Errors);
    End If;
End Sub UserProc;

After executing the example a rolling regression model with the following parameters is created:

Sample period is set manually.
The width of rolling regression window is four.
Window shift interval is one.

The console window displays matrix of coefficients estimation, statistic, probability, standard errors and forecasting series.