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 that involves 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
combination of ARCH and GARCH models with autoregressive models, with or without regressors
estimation and testing of general heteroscedasticity models
variety of model diagnostic information including the following:
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