A bootstrap-assisted spectral test of white noise under unknown dependence
AbstractTo test for the white noise null hypothesis, we study the Cramér-von Mises test statistic that is based on the sample spectral distribution function. Since the critical values of the test statistic are difficult to obtain, we propose a blockwise wild bootstrap procedure to approximate its asymptotic null distribution. Using a Hilbert space approach, we establish the weak convergence of the difference between the sample spectral distribution function and the true spectral distribution function, as well as the consistency of bootstrap approximation under mild assumptions. Finite sample results from a simulation study and an empirical data analysis are also reported.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 162 (2011)
Issue (Month): 2 (June)
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Web page: http://www.elsevier.com/locate/jeconom
Hypothesis testing Spectral distribution function Time series White noise Wild bootstrap;
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