Semiparametric Estimation and Inference for Trending I(D) and Related Processes

Abstract

This paper deals with estimation and hypothesis testing in models allowing for trending processes that are possibly nonstationary, nonlinear, and non-Gaussian. Using semi-parametric estimators, we obtain asymptotic confidence intervals for the trend and memory parameters, and we develop joint hypothesis testing for these. The confidence intervals are applicable for a wide class of processes, exhibit good coverage accuracy, and are easy to implement.