Approximate HPD Regions for Testing Residual Autocorrelation Using Augmented Regressions

  • L. Bauwens
  • A. Rasquero
Conference paper
Part of the Statistics and Computing book series (SCO)


We evaluate two tests of residual autocorrelation in the linear regression model in a Bayesian framework. Each test checks if an approximate highest posterior density region of the parameters of the autoregressive process of the error contains the null hypothesis. The approximation consists in computing the posterior density of the coefficients of the AR process using augmented regressions. The first test uses the initial regression augmented with its lagged Bayesian residuals and can be done with tables of the Fisher distribution. The second test augments the initial regression with lagged dependent and explanatory variables, and requires numerical integration. The tests are evaluated through a small Monte-Carlo experiment, which indicates that the first test (easier to compute) is more powerful than the second one.

Key Words

Residual Autocorrelation HPD Region Power Augmented Regressions Regression Model Bayesian Inference. 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • L. Bauwens
    • 1
  • A. Rasquero
    • 2
  1. 1.CORE, Université Catholique de LouvainLouvain-La-NeuveBelgium
  2. 2.GREQE, Ecole des Hautes Etudes en Sciences SocialesMarseilleFrance

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