Abstract
It is shown how the bootstrap can be applied to obtain a modified Durbin—Watson (MDW) test for serial correlation in a general regression model under departures from the assumption of normality. Critical values of the MDW test can be computed for each given design matrix, irrespective of the form of the underlying error distribution. Its performance is illustrated by numerical examples and some Monte Carlo simulations, applying typical economic data which has previously been used in the literature. Among others, it is found that the suggested test is more robust and more powerful than existing non parametric tests.
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