Sowell, Fallaw (2006): The Empirical Saddlepoint Approximation for GMM Estimators.

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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, nonnormal 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.
Item Type:  MPRA Paper 

Institution:  Carnegie Mellon University 
Original Title:  The Empirical Saddlepoint Approximation for GMM Estimators 
Language:  English 
Keywords:  Generalized method of moments estimator; test of overidentifying restrictions; sampling distribution; empirical saddlepoint approximation; asymptotic distribution 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General 
Item ID:  3356 
Depositing User:  Fallaw Sowell 
Date Deposited:  30. May 2007 
Last Modified:  22. Apr 2015 08:02 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/3356 