A bootstrap approach to moment selection
AbstractThis paper proposes a bootstrap-based procedure for selecting the best moment conditions among a set of correctly specified moment conditions. The proposed method can be motivated by Edgeworth expansions and chooses moment conditions that minimize the approximate coverage error of confidence intervals and hypothesis tests for parameters. Because of the analytical intractability of Edgeworth expansions, we estimate the coverage error by the bootstrap. The proposed method can be applied to a wide class of estimators for possibly non-linear and possibly dynamic models. We investigate the small sample performance of the proposed method in Monte Carlo experiments. Copyright Royal Economic Society 2006 Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA, 02148, USA.
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Bibliographic InfoArticle provided by Royal Economic Society in its journal The Econometrics Journal.
Volume (Year): 9 (2006)
Issue (Month): 1 (03)
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- Moreira, Marcelo J. & Porter, Jack R. & Suarez, Gustavo A., 2009. "Bootstrap validity for the score test when instruments may be weak," Journal of Econometrics, Elsevier, vol. 149(1), pages 52-64, April.
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- Canay, Ivan A., 2010. "Simultaneous selection and weighting of moments in GMM using a trapezoidal kernel," Journal of Econometrics, Elsevier, vol. 156(2), pages 284-303, June.
- Kuersteiner, Guido M., 2012. "Kernel-weighted GMM estimators for linear time series models," Journal of Econometrics, Elsevier, vol. 170(2), pages 399-421.
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