Munich Personal RePEc Archive

The Empirical Saddlepoint Approximation for GMM Estimators

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

[img]
Preview
PDF
MPRA_paper_3356.pdf

Download (394kB) | Preview

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.

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.