A bootstrap-assisted spectral test of white noise under unknown dependence

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Pages (from-to)213-224
Number of pages12
JournalJournal of Econometrics
Volume162
Issue number2
DOIs
StatePublished - Jun 2011

Keywords

  • Hypothesis testing
  • Spectral distribution function
  • Time series
  • White noise
  • Wild bootstrap

ASJC Scopus subject areas

  • Economics and Econometrics

Fingerprint Dive into the research topics of 'A bootstrap-assisted spectral test of white noise under unknown dependence'. Together they form a unique fingerprint.

Cite this