The VARMAX Procedure
- Overview
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Getting Started
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Syntax
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Details
Missing Values VARMAX Model Dynamic Simultaneous Equations Modeling Impulse Response Function Forecasting Tentative Order Selection VAR and VARX Modeling Bayesian VAR and VARX Modeling VARMA and VARMAX Modeling Model Diagnostic Checks Cointegration Vector Error Correction Modeling I(2) Model Multivariate GARCH Modeling Output Data Sets OUT= Data Set OUTEST= Data Set OUTHT= Data Set OUTSTAT= Data Set Printed Output ODS Table Names ODS Graphics Computational Issues
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Examples
- References
| GARCH Statement |
The GARCH statement specifies a GARCH-type multivariate conditional heteroscedasticity model.
The following options can be used in the GARCH statement.
- FORM=value
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specifies the representation for a GARCH model. Valid values are as follows:
- BEKK
specifies a BEKK representation. This is the default.
- CCC
specifies a constant conditional correlation representation.
- OUTHT=SAS-data-set
writes the conditional covariance matrix to an output data set.
- P=number
- P=(number-list)
specifies the order of the process or the subset of GARCH terms to be fitted. For example, you can specify the P=(1,3) option. The P=3 option is equivalent to the P=(1,2,3) option. The default is P=0.
- Q=number
- Q=(number-list)
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specifies the order of the process or the subset of ARCH terms to be fitted. This option is required in the GARCH statement. For example, you can specify the Q=(2) option. The Q=2 option is equivalent to the Q=(1,2) option.
For the VAR(1)–ARCH(1) model,
model y1 y2 / p=1; garch q=1 form=bekk;
For the multivariate GARCH(1,1) model,
model y1 y2; garch q=1 p=1 form=ccc;
Other multivariate GARCH-type models are
model y1 y2 = x1 / xlag=1; garch q=1;
model y1 y2 / q=1; garch q=1 p=1;
See the section Multivariate GARCH Modeling for details.