B0691
Title: Bootstrapping Whittle estimators
Authors: Efstathios Paparoditis - University of Cyprus (Cyprus) [presenting]
Jens-Peter Kreiss - Technische Universitaet Braunschweig (Germany)
Abstract: Fitting parametric models by optimizing frequency domain objective function is an attractive approach to parameter estimation in time series analysis. Whittle estimators are a prominent example in this context. Under weak conditions and the assumption that the true spectral density of the underlying process does not necessarily belong to the parametric class of spectral densities fitted, the distribution of Whittle estimators typically depends on the difficulty in estimating characteristics of the underlying process. This makes the implementation of asymptotic results for the construction of confidence intervals or for assessing the variability of estimators, difficult in practice. A frequency domain bootstrap method is proposed to estimate the distribution of Whittle estimators, which is asymptotically valid under assumptions that not only allow for possible model misspecification but also for weak dependence conditions which are satisfied by a wide range of stationary stochastic processes. Adaptions of the bootstrap procedure developed to incorporate different modifications of Whittle estimators proposed in the literature, like, for instance, tapered, de-biased or boundary-extended Whittle estimators, are also considered. Simulations demonstrate the capabilities of the bootstrap method proposed and its good finite sample performance. A real-life data analysis also is presented.
