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

62 Pages Posted: 10 Jan 2020 Last revised: 16 Oct 2021

See all articles by Davide La Vecchia

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

O. Scaillet

Swiss Finance Institute - University of Geneva

Date Written: December 17, 2019

Abstract

We develop theory of a novel fast bootstrap for dependent data. Our scheme deploys i.i.d. resampling of smoothed moment indicators. We characterize the class of parametric and semiparametric estimation problems for which the method is valid. We show the asymptotic refinements of the new 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 M-estimation, generalized method of moments, and generalized empirical likelihood estimation. In a Monte Carlo study, we consider an autoregressive conditional duration model and we compare our method with other extant, routinely-applied first- and higher-order correct methods. The results provide numerical evidence that the novel bootstrap yields higher-order accurate confidence intervals, while remaining computationally lighter than its higher-order correct competitors. A real-data example on dynamics of trading volume of US stocks illustrates the empirical relevance of our method.

Keywords: Fast bootstrap methods, Higher-order re nements, Generalized Empirical Likelihood, Con dence distributions, Mixing processes

JEL Classification: C12, C15, C22, C52, C58, G12

Suggested Citation

La Vecchia, Davide and Moor, Alban and Scaillet, Olivier, A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data (December 17, 2019). Swiss Finance Institute Research Paper No. 20-01, Available at SSRN: https://ssrn.com/abstract=3515288 or http://dx.doi.org/10.2139/ssrn.3515288

Davide La Vecchia

University of Geneva - Geneva School of Economics and Management - Research Center for Statistics ( email )

Bld Pont d'Arve 40
Genève, CH - 1205
Switzerland

Alban Moor

University of Geneva - Research Center for Statistics ( email )

Geneva
Switzerland

Olivier Scaillet (Contact Author)

Swiss Finance Institute - University of Geneva ( email )

Geneva
Switzerland

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