Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models

UCSD Department of Economics Working paper 2000-32R

45 Pages Posted: 17 Jan 2001

See all articles by Sílvia Gonçalves

Sílvia Gonçalves

University of Montreal - Department of Economics

Halbert L. White, Jr.

University of California, San Diego (UCSD) - Department of Economics

Date Written: December 2000

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

JEL Classification: C15, C22

Suggested Citation

Goncalves, Silvia and White, Halbert L., Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models (December 2000). UCSD Department of Economics Working paper 2000-32R. Available at SSRN: https://ssrn.com/abstract=256077 or http://dx.doi.org/10.2139/ssrn.256077

Silvia Goncalves (Contact Author)

University of Montreal - Department of Economics ( email )

C.P. 6128, succursale Centre-Ville
Montreal, Quebec H3C 3J7
Canada

Halbert L. White

University of California, San Diego (UCSD) - Department of Economics ( email )

9500 Gilman Drive
La Jolla, CA 92093-0508
United States
858-534-3502 (Phone)
858-534-7040 (Fax)

HOME PAGE: http://www.econ.ucsd.edu/~mbacci/white/

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