Abstract
To test for the white noise null hypothesis, we study the Cramrvon 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.
Original language | English (US) |
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Pages (from-to) | 213-224 |
Number of pages | 12 |
Journal | Journal of Econometrics |
Volume | 162 |
Issue number | 2 |
DOIs | |
State | Published - Jun 2011 |
Keywords
- Hypothesis testing
- Spectral distribution function
- Time series
- White noise
- Wild bootstrap
ASJC Scopus subject areas
- Economics and Econometrics