Davide La Vecchia (University of Geneva)
Saddlepoint techniques for dependent data
Saddlepoint techniques provide numerically accurate, higher-order, small sample approximations to the distribution of estimators and test statistics. While a rich theory is available for saddlepoint techniques in the case of independently and identically distributed observations, only a few results have been obtained for dependent data. In this talk, we explain how to fill this gap in the literature. Using the method of the tilted-Edgeworth expansion, we devise new saddlepoint density approximations and saddlepoint test statistics in the settings of time series (short or long memory) and spatial processes (panel data models, with fixed effects, time-varying covariates and spatially correlated errors). We compare our new approximations to the ones obtained by standard asymptotic theory, by Edgeworth expansion and by resampling methods. The numerical exercises illustrate that our approximations yield accuracy's improvements, while preserving analytical tractability.