ISmCointegrationEq.MissingData

Fore Syntax

MissingData: IMissingData;

Fore.NET Syntax

MissingData: Prognoz.Platform.Interop.Stat.IMissingData;

Description

The MissingData property returns parameters of missing data treatment in the explained series.

Comments

By default missing data is not treated.

Fore Example

Add a link to the Stat system assembly.

Sub UserProc;
Var
    CointegrEq: ISmCointegrationEq;
    y: Array[11] Of Double;
    x1: Array[21] Of Double;
    AR_X, AR_Y: Array[1] Of Integer;
    CommonEx, LongTerm: Array[1] Of Integer;
    Res: Integer;
    Eq: ISlEquation;
    VARStat: IVARStatistics;
Begin
    CointegrEq := New SmCointegrationEq.Create;
    // Endogenous variable:
    y[00] := 95;  y[01] := 45;  y[02] := Double.Nan;
    y[03] := -36; y[04] := 10;  y[05] := -15;
    y[06] := 36;  y[07] := -10; y[08] := Double.Nan;
    y[09] := -44; y[10] := -7;
    // Exogenous variable:
    x1[00] := 6;  x1[01] := 8;  x1[02] := 10;
    x1[03] := 5;  x1[04] := 3;  x1[05] := 6;
    x1[06] := 3;  x1[07] := 7;  x1[08] := 8;
    x1[09] := 10; x1[10] := 5;  x1[11] := 2;
    x1[12] := 1;  x1[13] := 1;  x1[14] := 3;
    x1[15] := 4;  x1[16] := 7;  x1[17] := 4;
    x1[18] := 7;  x1[19] := 4;  x1[20] := 3;
    Eq := CointegrEq.Equation;
    // Output series
    Eq.Serie.Value := y;
    // Exogenous variable:
    Eq.ExogenousVariables.Add.Value := x1;
    AR_Y[0] := 1;
    AR_X[0] := 0;
    // Autoregression order of endogenous variable:
    CointegrEq.SerieAROrder := AR_Y;
    //Autoregression order of exogenous variable:
    Eq.AutoRegressionOrder := AR_X;
    // Sample period:
    CointegrEq.Period.FirstPoint := 0;
    CointegrEq.Period.LastPoint := 11;
    // Forecast:
    Eq.Forecast.LastPoint := 21;
    // Include the first exogenous variable into short-term links group
    CommonEx[0] := 0;
    CointegrEq.CommonExogenious := CommonEx;
    // Include the second exogenous variable into long-term links group
    LongTerm[0] := 0;
    CointegrEq.LongTermExogenious := LongTerm;
    // Missing data treatment:
    CointegrEq.MissingData.Method := MissingDataMethod.LinTrend;
    // Calculate model:
    Res := CointegrEq.Execute;
    If Res = 0 Then
        Debug.WriteLine("=== Values of vector autoregression statistics ===");
        VARStat := CointegrEq.VARStatistics;
        Debug.WriteLine("Covariance matrix determinant:");
        Debug.WriteLine(VARStat.RC);
        Debug.WriteLine("Residuals covariance matrix determinant:");
        Debug.WriteLine(VARStat.RCadj);
        Debug.WriteLine("Log-likelihood function:");
        Debug.WriteLine(VARStat.LLV);
        Debug.WriteLine("Akaike information criterion:");
        Debug.WriteLine(VARStat.AIC);
        Debug.WriteLine("Schwarz criterion:");
        Debug.WriteLine(VARStat.SC);
        Else
            Debug.Writeline(CointegrEq.Errors);
    End If;
End Sub UserProc;

After executing the example the console window displays vector autoregression statistics values.

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
    CointegrEq: ISmCointegrationEq;
    y: Array[11] Of Double;
    x1: Array[21] Of Double;
    AR_X, AR_Y: Array[1] Of Integer;
    CommonEx, LongTerm: Array[1] Of Integer;
    Res: Integer;
    Eq: ISlEquation;
    VARStat: IVARStatistics;
Begin
    CointegrEq := New SmCointegrationEq.Create();
    // Endogenous variable:
    y[00] := 95;  y[01] := 45;  y[02] := Double.Nan;
    y[03] := -36; y[04] := 10;  y[05] := -15;
    y[06] := 36;  y[07] := -10; y[08] := Double.Nan;
    y[09] := -44; y[10] := -7;
    // Exogenous variable:
    x1[00] := 6;  x1[01] := 8;  x1[02] := 10;
    x1[03] := 5;  x1[04] := 3;  x1[05] := 6;
    x1[06] := 3;  x1[07] := 7;  x1[08] := 8;
    x1[09] := 10; x1[10] := 5;  x1[11] := 2;
    x1[12] := 1;  x1[13] := 1;  x1[14] := 3;
    x1[15] := 4;  x1[16] := 7;  x1[17] := 4;
    x1[18] := 7;  x1[19] := 4;  x1[20] := 3;
    Eq := CointegrEq.Equation;
    // Output series
    Eq.Serie.Value := y;
    // Exogenous variable:
    Eq.ExogenousVariables.Add().Value := x1;
    AR_Y[0] := 1;
    AR_X[0] := 0;
    // Autoregression order of endogenous variable:
    CointegrEq.SerieAROrder := AR_Y;
    //Autoregression order of exogenous variable:
    Eq.AutoRegressionOrder := AR_X;
    // Sample period:
    CointegrEq.Period.FirstPoint := 0;
    CointegrEq.Period.LastPoint := 11;
    // Forecast:
    Eq.Forecast.LastPoint := 21;
    // Include the first exogenous variable into short-term links group
    CommonEx[0] := 0;
    CointegrEq.CommonExogenious := CommonEx;
    // Include the second exogenous variable into long-term links group
    LongTerm[0] := 0;
    CointegrEq.LongTermExogenious := LongTerm;
    // Missing data treatment:
    CointegrEq.MissingData.Method := MissingDataMethod.mdmLinTrend;
    // Calculate model:
    Res := CointegrEq.Execute();
    If Res = 0 Then
        System.Diagnostics.Debug.WriteLine("=== Values of vector autoregression statistics ===");
        VARStat := CointegrEq.VARStatistics;
        System.Diagnostics.Debug.WriteLine("Covariance matrix determinant:");
        System.Diagnostics.Debug.WriteLine(VARStat.RC);
        System.Diagnostics.Debug.WriteLine("Residuals covariance matrix determinant:");
        System.Diagnostics.Debug.WriteLine(VARStat.RCadj);
        System.Diagnostics.Debug.WriteLine("Likelihood function algorithm:");
        System.Diagnostics.Debug.WriteLine(VARStat.LLV);
        System.Diagnostics.Debug.WriteLine("Akaike information criterion:");
        System.Diagnostics.Debug.WriteLine(VARStat.AIC);
        System.Diagnostics.Debug.WriteLine("Schwarz criterion:");
        System.Diagnostics.Debug.WriteLine(VARStat.SC);
        Else
            System.Diagnostics.Debug.Writeline(CointegrEq.Errors);
    End If;
End Sub;