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The Application of the Durbin-Watson Test to the Dynamic Regression Model Under Normal and Non-Normal Errors

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  • King, Maxwell L.
  • Harris, David C.

Abstract

Until recently, a difficulty with applying the Durbin-Watson (DW) test to the dynamic linear regression model has been the lack of appropriate critical values. Inder (1986) used a modified small-disturbance distribution (SDD) to find approximate criticl values. King and Wu (1991) showed that the exact SDD of the DW statistic is equivalent to the distribution of the DW statistic from the regression with the lagged dependent variables replaced by their means. Unfortunately, these means are unknown although they could be estimated by the actual variable values. This provides a justification for using the exact critical values of the DW statistic from the regression with the lagged. dependent variables treated as non-stochastic regressors. Extensive Monte Carlo experiments are reported in this paper. They show that this approach leads to reasonably accurate critical values, particularly when two lags of the dependent variable are present. Robustness to non-normality is also investigated.

Suggested Citation

  • King, Maxwell L. & Harris, David C., "undated". "The Application of the Durbin-Watson Test to the Dynamic Regression Model Under Normal and Non-Normal Errors," Department of Econometrics and Business Statistics Working Papers 267759, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:ags:monebs:267759
    DOI: 10.22004/ag.econ.267759
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