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

Authors: Paulo Parente and Richard J. Smith
Date: 30 October 2019
Type: cemmap Working Paper, CWP60/19
DOI: 10.1920/wp.cem.2019.6019

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.

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