ISlARMA.CalcInitMode

Syntax

Description

The CalcInitMode property selects method of determining initial approximations.

Comments

This property is relevant if the model includes exogenous variables or a constant.

Example

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

Sub UserProc;
Var
    lr: ISmLinearRegress;
    W: Array[12] Of Double;
    X: array[20] Of Double;
    ARMA: ISlARMA;
    AR, MA: Array[1] Of Integer;
    Inits: Array[1] Of Double;
    res: Integer;
    d: Double;
    CoefficientsAR, CoefficientsMA, CoefficientsX: ICoefficients;
    ModelCoefficients: IModelCoefficients;
Begin
    lr := New SmLinearRegress.Create;
     // explained series values

    w[0] := 2; w[4] := -1.9; w[8] := -0.7;
    w[1] := 0.8; w[5] := Double.Nan; w[9] := Double.Nan;
    w[2] := -0.3; w[6] := 3.2; w[10] := 4.3;
    w[3] := -0.3; w[7] := 1.6; w[11] := 1.1;
    lr.Explained.Value := w;
     // explanatory series values
    x[0] := Double.Nan; x[10] := 11;
    x[1] := 2; x[11] := 12;
    x[2] := 3; x[12] := 13;
    x[3] := 4; x[13] := Double.Nan;
    x[4] := 5; x[14] := 15;
    x[5] := 6; x[15] := 16;
    x[6] := Double.Nan; x[16] := 17;
    x[7] := 8; x[17] := Double.Nan;
    x[8] := 9; x[18] := 19;
    x[9] := 10; x[19] := 20;
     // sample period

    lr.ModelPeriod.FirstPoint := 1;
    lr.ModelPeriod.LastPoint := 12;
    lr.Forecast.LastPoint := 19;
     // missing data treatment method
    lr.MissingData.Method := MissingDataMethod.AnyValue;
     // exogenous variable is used in model
    lr.Explanatories.Clear;
    lr.Explanatories.Add.Value := X;
     // initial approximation of exogenous variable
    lr.Explanatories.Item(0).InitValue := 0.7;
    ModelCoefficients := lr.ModelCoefficients;
     // constant is used in model
    ModelCoefficients.Intercept.Mode := InterceptMode.AutoEstimate;
     // initial approximation for constant
    ModelCoefficients.Intercept.InitValue := 3;
    ARMA := lr.ARMA;
     // autoregression order
    AR[0] := 2;
    ARMA.OrderAR := AR;
     // moving average order
    MA[0] := 1;
    ARMA.OrderMA := MA;
     // initial approximation definition method
    ARMA.CalcInitMode := ARMAInitType.Manual;
     // initial approximation of autoregression
    Inits[0] := 0.2;
    ARMA.InitAR := Inits;
     // initial approximations of moving average
    Inits[0] := 0.3;
    ARMA.InitMA := Inits;
     // optimization method

    ARMA.EstimationMethod := ARMAEstimationMethodType.GaussNewton;
     //number of iterations for optimization method
    ARMA.MaxIteration := 50;
     //accuracy for optimization method
    ARMA.Tolerance := 0.1;
     // model calculation
    res := lr.Execute;
    Debug.WriteLine(lr.Errors);
    If (res = 0) Then
         // autoregression coefficients
        Debug.WriteLine("Autoregression coefficients estimates");
        CoefficientsAR := ARMA.CoefficientsAR;
        d := CoefficientsAR.Estimate[0];
        Debug.WriteLine(" Value: " + d.ToString);
        d := CoefficientsAR.StandardError[0];
        Debug.WriteLine(" Standard error: " + d.ToString);
        d := CoefficientsAR.TStatistic[0];
        Debug.WriteLine(" t-statistic: " + d.ToString);
        d := CoefficientsAR.Probability[0];
        Debug.WriteLine(" Probability: " + d.ToString);

         // moving average coefficients
        Debug.WriteLine("Estimates of moving average coefficients");
        CoefficientsMA := ARMA.CoefficientsMA;
        d := CoefficientsMA.Estimate[0];
        Debug.WriteLine(" Value: " + d.ToString);
        d := CoefficientsMA.StandardError[0];
        Debug.WriteLine(" Standard error: " + d.ToString);
        d := CoefficientsMA.TStatistic[0];
        Debug.WriteLine(" t-statistic: " + d.ToString);
        d := CoefficientsMA.Probability[0];
        Debug.WriteLine(" Probability: " + d.ToString);
         // exogenous variable coefficients
        Debug.WriteLine("Estimates of coefficients X:");
        CoefficientsX := ModelCoefficients.Coefficients;
        d := CoefficientsX.Estimate[0];
        Debug.WriteLine(" Value: " + d.ToString);
        d := CoefficientsX.StandardError[0];
        Debug.WriteLine(" Standard error: " + d.ToString);
        d := CoefficientsX.TStatistic[0];
        Debug.WriteLine(" t-statistic: " + d.ToString);
        d := CoefficientsX.Probability[0];
        Debug.WriteLine(" Probability: " + d.ToString);
         // constant
        d := ModelCoefficients.Intercept.Estimate;
        Debug.WriteLine("Constant: " + d.ToString);
    End If;
End Sub UserProc;

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

Initial approximations are specified manually.
The Quasi-Newton method is used as an optimization method.
Maximum iterations for model calculation is 50.
Autoregression order equals to 2.
Moving average order equals to 1.
Calculation accuracy equals to 0.1.

The console window displays model coefficients estimates and the constant value.