Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models

Paulo Parente and Richard J. Smith

Journal of Time Series Analysis, 2021, vol. 42, issue 4, 377-405

Abstract: This article applies a novel bootstrap method, the kernel block bootstrap (KBB), to quasi‐maximum likelihood (QML) estimation of dynamic models with stationary strong mixing data. The method first kernel weights the components comprising the quasi‐log likelihood function in an appropriate way and then samples the resultant transformed components using the standard ‘m out of n’ bootstrap. We investigate the first‐order asymptotic properties of the KBB method for QML demonstrating, in particular, its consistency and the first‐order asymptotic validity of the bootstrap approximation to the distribution of the QML estimator. A set of simulation experiments for the mean regression model illustrates the efficacy of the kernel block bootstrap for QML estimation.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/jtsa.12573

Related works:
Working Paper: Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models (2019)
Working Paper: Quasi-Maximum Likelihood and the Kernel Block Bootstrap for Nonlinear Dynamic Models (2018)
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:bla:jtsera:v:42:y:2021:i:4:p:377-405

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().