Premium
Bartlett Correction of Empirical Likelihood for Non‐Gaussian Short‐Memory Time Series
Author(s) -
Chen Kun,
Chan Ngai Hang,
Yau Chun Yip
Publication year - 2016
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12175
Subject(s) - mathematics , empirical likelihood , series (stratigraphy) , statistics , gaussian , variance (accounting) , likelihood ratio test , econometrics , edgeworth series , confidence interval , gaussian process , maximum likelihood , paleontology , physics , accounting , quantum mechanics , business , biology
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 ).