Empirical Likelihood Block Bootstrapping

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Empirical Likelihood Block Bootstrapping

Author Info

Jason Allen
Allan W. Gregory
Katsumi Shimotsu

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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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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
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Handle: RePEc:qed:wpaper:1156

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Paper
- Jason Allen & Allan W. Gregory & Katsumi Shimotsu, 2008. "Empirical Likelihood Block Bootstrapping," Working Papers 08-18, Bank of Canada. [Downloadable!]

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series 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 listed on IDEAS
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