QUASI-MAXIMUM LIKELIHOOD AND THE KERNEL BLOCK BOOTSTRAP FOR NONLINEAR DYNAMIC MODELS

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.