Statistical Inference for Long Memory and Nonlinear Time Series

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

The proposal aims to develop methodological and theoretical tools for statistical inference of long memory and/or nonlinear time series, for which the traditional methods and theory developed for linear ARMA-type series are not known to be applicable. Since the applications of long memory and nonlinear models are rapidly growing, there is an urgent and crucial need to either provide a theoretical justification for existing methods or propose novel methods that are able to accommodate long memory and nonlinear features. To meet this need, the investigator proposes to study the following research problems: confidence interval for spectral means and ratio statistics; Whittle estimation and diagnostic checking for fractionally integrated time series with uncorrelated but dependent errors; new tests of independence and non-correlations between two stationary time series; frequency domain semiparametric inference for bivariate fractionally integrated nonlinear time series. All of them are linked to the second order properties of the long/short time series with nonlinear features, and together, they cover a wide spectrum of important inference issues for such series. Time series with long memory and nonlinearities occur in various fields, including atmosphere science, environmental science, geophysics, hydrology, economics, finance and others. This work will greatly enhance the available methodologies and theories, provide more tools and have potential applications in all such fields. The proposed research has significant impact on education through involvement of Ph.D students directly in the proposed research and incorporation of results into graduate statistical courses.