Previous Page | Next Page


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 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

Previous Page | Next Page | Top of Page