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Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models Author info | Abstract | Publisher info | Download info | Related research | Statistics Silvia Goncalves (University of Montreal)
Halbert White (University of California, San Diego)
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The bootstrap is an increasingly popular method for performing statistical inference. This paper provides the theoretical foundation for using the bootstrap as a valid tool of inference for quasi-maximum likelihood estimators (QMLE). We provide a unified framework for analyzing bootstrapped extremum estimators of nonlinear dynamic models for heterogeneous dependent stochastic processes. We apply our results to two block bootstrap methods, the moving blocks bootstrap of Künsch (1989) and Liu and Singh (1992) and the stationary bootstrap of Politis and Romano (1994), and prove the first order asymptotic validity of the bootstrap approximation to the true distribution of QML estimators. Further, these block bootstrap methods are shown to provide heteroskedastic and autocorrelation consistent standard errors for the QMLE, thus extending the already large literature on robust inference and covariance matrix estimation. We also consider bootstrap testing. In particular, we prove the first order asymptotic validity of the bootstrap distribution of a suitable bootstrap analog of a Wald test statistic for testing hypotheses.
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Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number
2000-32.
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Date of creation: 01 Dec 2000Date of revision:
Handle: RePEc:cdl:ucsdec:2000-32Note: oai:cdlib1:Contact details of provider: Postal: 9500 Gilman Drive, La Jolla, CA 92093-0508 Phone: (858) 534-3383 Fax: (858) 534-7040 Web page: http://repositories.cdlib.org/ucsdecon/ More information through EDIRC
For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).
Keywords: block bootstrap ; quasi maximum likelihood estimator ; nonlinear dynamic model ; near epoch dependence ; Wald test ; Other versions of this item:
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