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The AUTOREG Procedure

The AUTOREG procedure provides regression analysis and forecasting of linear models with autocorrelated or conditional heteroscedastic errors.

The AUTOREG procedure enables you to estimate and predict of linear regression models with autoregressive errors and to test linear hypotheses and estimate and test heteroscedasticity models. Additional features of PROC AUTOREG include

The AUTOREG procedure offers estimation and forecasting of autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), integrated GARCH (I-GARCH), exponential GARCH (E-GARCH), and GARCH-in-mean (GARCH-M) models. Exact gradients are used for GARCH-type model estimation. ARCH and GARCH models can be combined with autoregressive models, with or without regressors.

You can estimate and test heteroscedasticity models, and you can specify the flexible conditional variance from the GARCH model. In addition, you can

Using PROC AUTOREG, you can test for the stability of the regression coefficients and test for stationarity or unit roots in the time series. You can also specify initial parameter values for GARCH and heteroscedasticity models. Exact gradients are used for GARCH-type model estimation.

The AUTOREG procedure offers a variety of model diagnostic information, including

The AUTOREG procedure also offers embedded missing values.

For further details, see the SAS/ETS® User's Guide: The AUTOREG Procedure.