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Block Bootstrapping for Kernel Density Estimators under ψ-Weak Dependence

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  • Eunju Hwang
  • Dong Wan Shin

Abstract

Block bootstrap methods are applied to kernel-type density estimator and its derivatives for ψ-weakly dependent processes. Nonparametric density estimation is discussed via moving block bootstrap (MBB) and disjoint block bootstrap (DBB). Asymptotic validity is proved for MBB and DBB. A Monte-Carlo experiment compares confidence intervals based on MBB and DBB with an existing method based on normal approximation (NA) in terms of serial correlation, dynamic asymmetry, and conditional heteroscedasticity. The experiment shows that, in cases of substantial serial correlation, MBB and DBB perform better than NA and, in the other cases, MBB and DBB perform as good as NA.

Suggested Citation

  • Eunju Hwang & Dong Wan Shin, 2014. "Block Bootstrapping for Kernel Density Estimators under ψ-Weak Dependence," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(17), pages 3751-3761, September.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:17:p:3751-3761
    DOI: 10.1080/03610926.2012.701695
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