Quasi-Maximum Likelihood and The Kernel Block Bootstrap for Nonlinear Dynamic Models

Abstract

This paper (joint with Paulo M.D.C. Parente, ISEG, Lisbon School of Economics & Management) 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 first order asymptotic properties of the KBB method for quasi-maximum likelihood demonstrating, in particular, its consistency and the first-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.

Presenter

Professor Richard J. Smith, Cambridge University