Regression with Autocorrelated and Heteroscedastic Errors
The AUTOREG procedure provides regression analysis
and forecasting of linear models with autocorrelated or
heteroscedastic errors.
The AUTOREG procedure includes the following features:
- estimation and prediction of linear regression models
with autoregressive errors
- any order autoregressive or subset autoregressive process
- optional stepwise selection of autoregressive parameters
- choice of the following estimation methods:
- exact maximum likelihood
- exact nonlinear least squares
- Yule-Walker
- iterated Yule-Walker
- tests for any linear hypothesis involving the structural coefficients
- restrictions for any linear combination of the structural coefficients
- forecasts with confidence limits
- estimation and forecasting
of ARCH (autoregressive conditional heteroscedasticity),
GARCH (generalized autoregressive conditional heteroscedasticity),
I-GARCH (integrated GARCH), E-GARCH (exponential GARCH),
and GARCH-M (GARCH in mean) models
- ARCH and GARCH models can be combined with
autoregressive models, with or without regressors
- estimation and testing of general heteroscedasticity models
- variety of model diagnostic information including
- autocorrelation plots
- partial autocorrelation plots
- Durbin-Watson test statistic and generalized Durbin-Watson tests to any order
- Durbin h and Durbin t statistics
- Akaike information criterion
- Schwarz information criterion
- tests for ARCH errors
- Ramsey's RESET test
- Chow and PChow tests
- Phillips-Perron stationarity test
- CUSUM and CUMSUMSQ statistics
- exact significance levels (p-values) for
the Durbin-Watson statistic
- embedded missing values
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
