Saddlepoint techniques for the statistical analysis of time series

Abstract

Saddlepoint techniques provide numerically accurate, small sample approximations to the distribution of estimators and test statistics. While a complete theory on saddlepoint techniques is available in the case of independent observations, much less attention has been devoted to the time series setting. This talks contributes to fill this gap. Under short and/or long range serial dependence, for Gaussian and non Gaussian processes, the talk shows how to derive and implement saddlepoint approximations for Whittle's estimator, a frequency domain M-estimator. The derivation is based on the treatment of the standardized periodogram ordinates as (i.) i.d. random variables. Comparisons of the saddlepoint techniques to other methods are presented: the numerical exercises show that the saddlepoint approximations yield accuracy's improvements over extant methods, while preserving analytical tractability and avoiding resampling. The talks starts with a gentle introduction to saddlepoint techniques in the i.i.d. setting and with a review of the basic frequency domain tools for time series analysis. The results are based on joint works with E. Ronchetti and A. Moor.

Registration
You can sign up for this seminar by sending an email to eb-secr@ese.eur.nl. The lunch will be provided (vegetarian option included).