Bartlett Correction of Empirical Likelihood for Non‐Gaussian Short‐Memory Time Series |
| |
| Authors: | Kun Chen Ngai Hang Chan Chun Yip Yau |
| |
| Affiliation: | 1. School of Statistics, Southwestern University of Finance and Economics, Chengdu, China;2. Department of Statistics, The Chinese University of Hong Kong, Hong Kong |
| |
| Abstract: | Bartlett correction, which improves the coverage accuracies of confidence regions, is one of the desirable features of empirical likelihood. For empirical likelihood with dependent data, previous studies on the Bartlett correction are mainly concerned with Gaussian processes. By establishing the validity of Edgeworth expansion for the signed root empirical log‐likelihood ratio statistics, we show that the Bartlett correction is applicable to empirical likelihood for short‐memory time series with possibly non‐Gaussian innovations. The Bartlett correction is established under the assumptions that the variance of the innovation is known and the mean of the underlying process is zero for a single parameter model. In particular, the order of the coverage errors of Bartlett‐corrected confidence regions can be reduced from O(n?1) to O(n?2). |
| |
| Keywords: | Coverage error Edgeworth expansion higher‐order cumulants periodogram Whittle likelihood |
|
|
