ISmLinearRegress.GLSMatrix

Fore Syntax

GLSMatrix: Array;

Fore.NET Syntax

GLSMatrix: System.Array;

Description

The GLSMatrix property determines a covariance matrix for generalized least squares method.

Comments

A covariance matrix should be symmetric and positive definite.

Fore Example

Add a link to the Stat system assembly.

Sub UserProc;
Var
    OLS: ISmLinearRegress;
    can, fra, ger: Array[10] Of Double;
    omega: Array[10, 10] Of Double;
    res: Integer;
Begin
    OLS := New SmLinearRegress.Create;
    // Set values for variables
    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;
    omega[1, 1] := 0;
    omega[2, 2] := 0;
    omega[5, 5] := 0;
    // Sample period parameters
    OLS.ModelPeriod.FirstPoint := 1;
    OLS.ModelPeriod.LastPoint := 10;
    // Choose explained variable
    OLS.Explained.Value := can;
    // Choose regressors
    OLS.Explanatories.Clear;
    OLS.Explanatories.Add.Value := fra;
    OLS.Explanatories.Add.Value := ger;
    // Parameters of model coefficients
    OLS.ModelCoefficients.Intercept.Mode := InterceptMode.AutoEstimate;
    // Method of missing data treatment
    OLS.MissingData.Method := MissingDataMethod.LinTrend;
    // Value of weights use
    OLS.UseWeights := True;
    // Setting covariance matrix for generalized least squares method
    OLS.GLSMatrix := omega;
    // Type of least squares method
    OLS.LSType := LRLSType.GLS;
    res := OLS.Execute;
    Debug.WriteLine(OLS.Errors);
    Debug.WriteLine("Summary statistics");
    Debug.Indent;
Debug.WriteLine("Fisherstatistic:"+OLS.SummaryStatistics.Fstat.ToString);     Debug.WriteLine("Probability for Fisher statistics: " + OLS.SummaryStatistics.ProbFstat.ToString);
    Debug.Unindent;
End Sub UserProc;

As a result of the example execution, the following settings are defined:

Sample period.
Method of missing data treatment.
Parameters of model coefficients.
Covariance matrix for generalized least squares method.
Type of least squares method.

The console window shows summary statistics.

Fore.NET Example

The requirements and result of the Fore.NET example execution match with those in the Fore example.

Imports Prognoz.Platform.Interop.Stat;
…
Public Shared Sub Main(Params: StartParams);
Var
    OLS: ISmLinearRegress;
    PDLTerm: ISlPDLTerm;
    Explanatories: ISlSerie;
    can, fra, ger: Array[10] Of Double;
    omega: Array[10, 10] Of Double;
    arr: Array Of Double;
    res: Integer;
    str: String;
Begin
    OLS := New SmLinearRegress.Create();
    // Set values fornbsp;variables
    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;
    omega[1, 1] := 0;
    omega[2, 2] := 0;
    omega[5, 5] := 0;
    // Sample period parameters
    OLS.ModelPeriod.FirstPoint := 1;
    OLS.ModelPeriod.LastPoint := 10;
    // Choose explained variable
    OLS.Explained.Value := can;
    // Choose regressors
    OLS.Explanatories.Clear();
    OLS.Explanatories.Add().Value := fra;
    OLS.Explanatories.Add().Value := ger;
    // Parameters of model coefficients
    OLS.ModelCoefficients.Intercept.Mode := InterceptMode.imAutoEstimate;
    // Missing data treatment method
    OLS.MissingData.Method := MissingDataMethod.mdmLinTrend;
    // Value of weights use
    OLS.UseWeights := True;
    // Set covariance matrix for generalized least squares method
    OLS.GLSMatrix := omega;
    // OLS type
    OLS.LSType := LRLSType.lrlstGLS;
    res := OLS.Execute();
    System.Diagnostics.Debug.WriteLine(OLS.Errors);
    System.Diagnostics.Debug.WriteLine("Summary statistics");
    System.Diagnostics.Debug.Indent();
System.Diagnostics.Debug.WriteLine("Fisherstatistic:"+OLS.SummaryStatistics.Fstat.ToString());     System.Diagnostics.Debug.WriteLine("Fisher statistics probability: " + OLS.SummaryStatistics.ProbFstat.ToString());
    System.Diagnostics.Debug.Unindent();
    System.Diagnostics.Debug.WriteLine("Explained series");
    System.Diagnostics.Debug.Indent();
End Sub;