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