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Nonparametric Tests for the Independence of Regressors and Disturbances as Specification Tests

David Johnson; Robert McClelland

Bureau of Labor Statistics

The Review of Economics and Statistics 1997

We adapt techniques from the literature on chaos and nonlinear dynamics to detect misspecification in models of serially independent data by checking for dependence between the regressors and disturbances. Our tests are nonparametric in that they determine whether the distribution of the disturbances depends on the regressors without identifying a model of dependence or the distribution of the disturbances. In Monte Carlo simulations we find that these tests have good power against dependence caused by omitted variables, incorrect functional form, heteroskedasticity, and similar problems.We also apply our tests to detect misspecification in models of income imputation.

DOI
10.1162/003465397556719
Volume
79 (2)
Pages
335-340
Language
en
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