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

Paulo Parente and Richard J. Smith
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Richard J. Smith: Institute for Fiscal Studies

No CWP60/19, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood 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 rst order asymptotic properties of the kernel block bootstrap method for quasi-maximum likelihood demonstrating, in particular, its consistency and the rst-order asymptotic validity of the bootstrap approximation to the distribution of the quasi-maximum likelihood estimator. A set of simulation experiments for the mean regression model illustrates the efficacy of the kernel block bootstrap for quasi-maximum likelihood estimation.

Date: 2019-10-30
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Related works:
Journal Article: Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models (2021)
Working Paper: Quasi-Maximum Likelihood and the Kernel Block Bootstrap for Nonlinear Dynamic Models (2018)
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