Fallaw Sowell (Tepper School of Business, Carnegie Mellon University USA)

Vendredi 9 mai 2008 à 11h15, salle 5220

The empirical saddlepoint distribution provides an approximation to the joint 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 traditional identification assumptions are satisfied, the empirical saddlepoint distribution converges to the familiar asymptotic normal distribution. Unlike the absolute errors associated with the asymptotic normal distributions and the bootstrap, the empirical saddlepoint has a relative error which leads to a more accurate approximation, particularly in the tails. In finite sample Monte Carlo simulations, the empirical saddlepoint performs as well as, and often better than, the bootstrap.