Empirical Likelihood Approach for Non-Gaussian Locally Stationary Processes

An application of empirical likelihood method to non-Gaussian locally stationary processes is presented. Based on the central limit theorem for locally stationary processes, we calculate the asymptotic distribution of empirical likelihood ratio statistics. It is shown that empirical likelihood method enables us to make inference on various important indices in time series analysis. Furthermore, numerical studies indicate empirical likelihood method gives nice estimation results