Empirical Likelihood Block Bootstrapping
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.Download Info
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Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1156.Length: 34 pages
Date of creation: Mar 2008
Date of revision:
Handle: RePEc:qed:wpaper:1156
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Keywords: generalized methods of moments; empirical likelihood; block-bootstrap;Other versions of this item:
- Allen, Jason & Gregory, Allan W. & Shimotsu, Katsumi, 2011. "Empirical likelihood block bootstrapping," Journal of Econometrics, Elsevier, vol. 161(2), pages 110-121, April.
- Jason Allen & Allan W. Gregory & Katsumi Shimotsu, 2008. "Empirical Likelihood Block Bootstrapping," Working Papers 08-18, Bank of Canada.
- Allen, Jason & Gregory, Allan W. & Shimotsu, Katsumi, 2010. "Empirical Likelihood Block Bootstrapping," Discussion Papers 2010-01, Graduate School of Economics, Hitotsubashi University.
- 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)
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Bravo, Francesco & Crudu, Federico, 2012.
"Efficient bootstrap with weakly dependent processes,"
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Elsevier, vol. 56(11), pages 3444-3458.
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