Efficient bootstrap with weakly dependent processes

Abstract

The efficient bootstrap methodology is developed for overidentified moment conditions models with weakly dependent observation. The resulting bootstrap procedure is shown to be asymptotically valid and can be used to approximate the distributions of t-statistics, the J-statistic for overidentifying restrictions, and Wald, Lagrange multiplier and distance statistics for nonlinear hypotheses. The asymptotic validity of the efficient bootstrap based on a computationally less demanding approximate k-step estimator is also shown. The finite sample performance of the proposed bootstrap is assessed using simulations in an intertemporal consumption based asset pricing model.