Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models

Silvia Goncalves () and Halbert White

No 2000-32R, University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego

Abstract: We provide a unified framework for analyzing bootstrapped extremum estimators of nonlinear dynamic models for heterogeneous dependent stochastic processes. We apply our results to the moving blocks bootstrap of Kunsch (1989) and Liu and Singh (1992) and prove the first order asymptotic validity of the bootstrap approximation to the true distribution of quasi-maximum likelihood estimators. We also consider bootstrap testing. In particular, we prove the first order asymptotic validity of the bootstrap distribution of suitable bootstrap analogs of Wald and Lagrange Multiplier statistics for testing hypotheses.

Keywords: block bootstrap; quasi-maximum likelihood estimator; nonlinear dynamic model; near epoch dependence; Wald Test (search for similar items in EconPapers)
Date: 2002-02-25
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Related works:
Working Paper: Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models (2000)
Working Paper: Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models (2002)
Journal Article: Maximum likelihood and the bootstrap for nonlinear dynamic models (2004)
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Persistent link: http://EconPapers.repec.org/RePEc:cdl:ucsdec:2000-32r

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