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Abstract

Monte Carlo evidence has made it clear that asymptotic tests based on generalized method of moments (GMM) estimation have disappointing size. The problem is exacerbated when the moment conditions are serially correlated. Several block bootstrap techniques have been proposed to correct the problem, including Hall and Horowitz (1996) and Inoue and Shintani (2006). We propose an empirical likelihood block bootstrap procedure to improve inference where models are characterized by nonlinear moment conditions that are serially correlated of possibly infinite order. Combining the ideas of Kitamura (1997) and Brown and Newey (2002), the parameters of a model are initially estimated by GMM which are then used to compute the empirical likelihood probability weights of the blocks of moment conditions. The probability weights serve as the multinomial distribution used in resampling. The first-order asymptotic validity of the proposed procedure is proven, and a series of Monte Carlo experiments show it may improve test sizes over conventional block bootstrapping.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1156.pdf
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Bibliographic Info

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1156.

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Length: 34 pages
Date of creation: Mar 2008
Date of revision:
Handle: RePEc:qed:wpaper:1156

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Related research

Keywords: generalized methods of moments; empirical likelihood; block-bootstrap;

Other versions of this item:

Find related papers by JEL classification:

• C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
• C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models

This paper has been announced in the following NEP Reports:

• NEP-ALL-2008-03-08 (All new papers)
• NEP-ECM-2008-03-08 (Econometrics)

References

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Cited by:

1. Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
   
   • Francesco Bravo & Federico Crudu, 2012. "Efficient bootstrap with weakly dependent processes," Discussion Papers 12/08, Department of Economics, University of York.

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