A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data

Davide La Vecchia, Alban Moor and Olivier Scaillet
Additional contact information
Davide La Vecchia: University of Geneva - Geneva School of Economics and Management - Research Center for Statistics
Alban Moor: University of Geneva - Research Center for Statistics

No 20-01, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We develop and implement a novel fast bootstrap for dependent data. Our scheme is based on the i.i.d. resampling of the smoothed moment indicators. We characterize the class of parametric and semi-parametric estimation problems for which the method is valid. We show the asymptotic refinements of the proposed procedure, proving that it is higher-order correct under mild assumptions on the time series, the estimating functions, and the smoothing kernel. We illustrate the applicability and the advantages of our procedure for Generalized Empirical Likelihood estimation. As a by-product, our fast bootstrap provides higher-order correct asymptotic confidence distributions. Monte Carlo simulations on an autoregressive conditional duration model provide numerical evidence that the novel bootstrap yields higher-order accurate confidence intervals. A real-data application on dynamics of trading volume of stocks illustrates the advantage of our method over the routinely-applied first-order asymptotic theory, when the underlying distribution of the test statistic is skewed or fat-tailed.

Keywords: Fast bootstrap methods; Higher-order refinements; Generalized Empirical Likelihood; Confidence distributions; Mixing processes. (search for similar items in EconPapers)
JEL-codes: C12 C15 C22 C52 C58 G12 (search for similar items in EconPapers)
Pages: 53 pages
Date: 2020-01
New Economics Papers: this item is included in nep-ore
References: Add references at CitEc
Citations:

Downloads: (external link)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3515288 (application/pdf)

Related works:
Journal Article: A higher-order correct fast moving-average bootstrap for dependent data (2023)
Working Paper: A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data (2022)
Working Paper: A higher-order correct fast moving-average bootstrap for dependent data (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2001

Access Statistics for this paper

More papers in Swiss Finance Institute Research Paper Series from Swiss Finance Institute Contact information at EDIRC.
Bibliographic data for series maintained by Ridima Mittal ().