Tsls(Input: ITimeSeries;
Period: IMsPeriod;
ConstantValue: Variant;
AROrder: String;
MAOrder: String;
Casewise: MsCasewise;
Explanatories: Array): Variant;
Tsls(Context: Prognoz.Platform.Interop.Fore.ForeRuntimeContext;
Input: Prognoz.Platform.Interop.Ms.ITimeSeries;
Period: Prognoz.Platform.Interop.Ms.IMsPeriod;
ConstantValue: object;
AROrder: string;
MAOrder: string;
Casewise: Prognoz.Platform.Interop.Ms.MsCasewise;
Explanatories: array of object): object;
Context. Context. The parameter is used only in Fore.NET.
Input. Output variable.
Period. Period, at which the method is calculated. If the parameter is set to Null, the method is calculated at the entire time period.
ConstantValue. Constant used in calculations.
AROrder. Autoregression order.
MAOrder. Moving average order.
Casewise. Missing data treatment method.
Explanatories. Exogenous and instrumental variables.
The Tsls method models a variable with the help of linear regression (estimation by method of instrumental variables).
Use this method only in series calculation mode.
ConstantValue. The constant value can be determined by the user or estimated automatically. Use the IModelling.Estimate method to estimate values automatically. If the model must be calculated without constant, use the IModelling.None method.
AROrder, MAOrder. Parameters are set in the string view. Specify the numbers or ranges of autoregression or moving average orders separated by commas. The range of autoregression or moving average orders is specified via the sign -. For example: AROrder = 1-3,5.
MAOrder. If the moving average order is set, back-casting can be used to estimate its coefficients. Back-casting is used by default. If back-casting must be disabled, the MAOrder parameter must contain the string "backcast.No". For example: MAOrder = "1-4;backcast.No".
Explanatories. Exogenous and instrumental variables are specified via commas. Use the Null value to separate these types of variables. The number of instrumental variables must be not less than the number of exogenous ones.
Executing the example requires that the repository contains a modeling container with the MS identifier. This container contains a model with the MODEL_D identifier calculated using by determinate expression method and containing more than one factor.
Add links to the Metabase, Ms system assemblies.
Sub UserProc;
Var
Mb: IMetabase;
ModelSpace, ModelObj: IMetabaseObject;
Transf: IMsFormulaTransform;
Formula: IMsFormula;
Model: IMsModel;
Determ: IMsDeterministicTransform;
TransVar: IMsFormulaTransformVariable;
Slice: IMsFormulaTransformSlice;
TermInfo: IMsFormulaTermInfo;
Inp_1, Inp_2: String;
Expr: IExpression;
Begin
Mb := MetabaseClass.Active;
ModelSpace := Mb.ItemById("MS").Bind;
ModelObj := Mb.ItemByIdNamespace("MODEL_D", ModelSpace.Key).Edit;
Model := ModelObj As IMsModel;
Transf := Model.Transform;
Formula := Transf.FormulaItem(0);
Determ := Formula.Method As IMsDeterministicTransform;
TransVar := Transf.Inputs.Item(0);
Slice := TransVar.Slices.Item(0);
TermInfo := Transf.CreateTermInfo;
TermInfo.Slice := Slice;
TermInfo.Type := MsFormulaTermType.Pointwise;
Inp_1 := TermInfo.TermInnerText;
TransVar := Transf.Inputs.Item(1);
Slice := TransVar.Slices.Item(0);
TermInfo := Transf.CreateTermInfo;
TermInfo.Slice := Slice;
TermInfo.Type := MsFormulaTermType.Pointwise;
Inp_2 := TermInfo.TermInnerText;
Expr := Determ.Expression;
Expr.References := "Ms";
Expr.AsString := "Tsls(" + Inp_1 + ", SetPeriod(" +
"""" + "01.01.2000" + """" + "," + """" + "01.01.2015" + """" +
"), Estimate, """ + "1 - 3" + """, """ + "1; 3" +
""", MsCasewise.Yes, " + Inp_2 +
", Null, " + Inp_2 + ")";
If Expr.Valid Then
ModelObj.Save;
Else
Debug.WriteLine("Model is not saved: error in formula");
End If;
End Sub UserProc;
After executing the example the model models the first input variable using linear regression (instrumental variables estimation) for the period from 2000 to 2015. Calculation is executed using the Casewise missing data treatment method.
The requirements and result of the Fore.NET example execution match with those in the Fore example.
Imports Prognoz.Platform.Interop.Ms;
Imports Prognoz.Platform.Interop.ForeSystem;
…
Public Shared Sub Main(Params: StartParams);
Var
Mb: IMetabase;
ModelSpace, ModelObj: IMetabaseObject;
Transf: IMsFormulaTransform;
Formula: IMsFormula;
Model: IMsModel;
Determ: IMsDeterministicTransform;
TransVar: IMsFormulaTransformVariable;
Slice: IMsFormulaTransformSlice;
TermInfo: IMsFormulaTermInfo;
Inp_1, Inp_2: String;
Expr: IExpression;
Begin
Mb := Params.Metabase;
ModelSpace := Mb.ItemById["MS"].Bind();
ModelObj := Mb.ItemByIdNamespace["MODEL_D", ModelSpace.Key].Edit();
Model := ModelObj As IMsModel;
Transf := Model.Transform;
Formula := Transf.FormulaItem[0];
Determ := Formula.Method As IMsDeterministicTransform;
TransVar := Transf.Inputs.Item[0];
Slice := TransVar.Slices.Item[0];
TermInfo := Transf.CreateTermInfo();
TermInfo.Slice := Slice;
TermInfo.Type := MsFormulaTermType.mfttPointwise;
Inp_1 := TermInfo.TermInnerText;
TransVar := Transf.Inputs.Item[1];
Slice := TransVar.Slices.Item[0];
TermInfo := Transf.CreateTermInfo();
TermInfo.Slice := Slice;
TermInfo.Type := MsFormulaTermType.mfttPointwise;
Inp_2 := TermInfo.TermInnerText;
Expr := Determ.Expression;
Expr.References := "Ms";
Expr.AsString := "Tsls(" + Inp_1 + ", SetPeriod(" +
"""" + "01.01.2000" + """" + "," + """" + "01.01.2015" + """" +
"), Estimate, """ + "1 - 3" + """, """ + "1; 3" +
""", MsCasewise.Yes, " + Inp_2 +
", Null, " + Inp_2 + ")";
If Expr.Valid Then
ModelObj.Save();
Else
System.Diagnostics.Debug.WriteLine("Model is not saved: error in the formula");
End If;
End Sub;
Expression 1:
Tsls({Brazil|BCA[t]},SetPeriod("01.01.2000","01.01.2015"),Estimate,"","",MsCasewise.Yes,{China|BCA},Null,{Japan|BCA})
Result: the Brazil|BCA series is modeled using linear regression method (instrumental variables estimation) by the following parameters: constant will be estimated using the IModelling.Estimate method, autoregression and moving average orders are not set, exogenous variable is the China|BCA factor, instrumental variable is the Japan|BCA factor, calculation is executed for the period from 2000 to 2015 using the Casewise data treatment method.
Use: it can be used in formulas of calculated series of time series database and in formulas of attribute-based models of modeling container.
Expression 2:
Tsls(X1,None,"1","2;backcast.No",MsCasewise.Yes,X4,Null,X2, X3)
Result: the X1 factor is modeled by the linear regression method (instrumental variables estimation) by the following parameters: the constant is not set, autoregression order is 1, moving average order is 2, back-casting is not used for the moving average coefficient estimation, the exogenous variable is the X4 factor, the instrumental variables are the X2 and the X3 factors; calculation is executed by using the Casewise missing data treatment method for the entire period.
Use: it can be used in model variable-based formulas of modeling container.
See also:
IModelling | Instrumental variables method | Time Series Database: Calculator Modeling Container: The Linear Regression (Instrumental Variables Estimation)Model, Editing Regressor or Formula