The Empirical Saddlepoint Approximation for GMM Estimators

Fallaw Sowell

MPRA Paper from University Library of Munich, Germany

Abstract: The empirical saddlepoint distribution provides an approximation to the sampling distributions for the GMM parameter estimates and the statistics that test the overidentifying restrictions. The empirical saddlepoint distribution permits asymmetry, non-normal tails, and multiple modes. If identification assumptions are satisfied, the empirical saddlepoint distribution converges to the familiar asymptotic normal distribution. In small sample Monte Carlo simulations, the empirical saddlepoint performs as well as, and often better than, the bootstrap. The formulas necessary to transform the GMM moment conditions to the estimation equations needed for the saddlepoint approximation are provided. Unlike the absolute errors associated with the asymptotic normal distributions and the bootstrap, the empirical saddlepoint has a relative error. The relative error leads to a more accurate approximation, particularly in the tails.

Keywords: Generalized method of moments estimator; test of overidentifying restrictions; sampling distribution; empirical saddlepoint approximation; asymptotic distribution (search for similar items in EconPapers)
JEL-codes: C12 C13 C5 (search for similar items in EconPapers)
Date: 2006-07, Revised 2007-05
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

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