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A nonparametric distributionf-free test for serial independence of errors

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2014-09-03
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Taylor & Francis
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Abstract
In this article, we propose a test for the serial independence of unobservable errors in location-scale models. We consider a Hoeffding-Blum-Kiefer-Rosenblat type empirical process applied to residuals, and show that under certain conditions it converges weakly to the same limit as the process based on true errors. We then consider a generalized spectral test applied to estimated residuals, and get a test that is asymptotically distribution-free and powerful against any type of pairwise dependence at all lags. Some Monte Carlo simulations validate our theoretical findings.
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Empirical processes, Generalized spectral test, Location-scale model, Parameter estimation uncertainty, Serial dependence, Unobservable errors
Bibliographic citation
Du, Z., & Escanciano, J. C. (2014). A Nonparametric Distribution-Free Test for Serial Independence of Errors. Econometric Reviews, 34(6-10), pp. 1011-1034.