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

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.

Issue Date:
Aug 01 1995
Publication Type:
Working or Discussion Paper
DOI and Other Identifiers:
Record Identifier:
Total Pages:
JEL Codes:
C12; C32
Series Statement:
Working Paper 6/95

 Record created 2018-02-02, last modified 2020-10-28

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