A review of empirical likelihood methods for time series

被引:33
|
作者
Nordman, Daniel J. [1 ]
Lahiri, Soumendra N. [2 ]
机构
[1] Iowa State Univ, Dept Stat, Ames, IA 50011 USA
[2] N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA
关键词
Blocking; Frequency domain; GARCH; High dimensional data; Kullback-Leibler distance; Long range dependence; Mahalanobis distance; Markov chains; Spatial data; Tapering; Whittle estimation; WEAKLY DEPENDENT PROCESSES; LONG-RANGE DEPENDENCE; PROBABILITY DENSITY-FUNCTIONS; GENERAL ESTIMATING EQUATIONS; RATIO CONFIDENCE-REGIONS; LARGE-SAMPLE PROPERTIES; AUTOREGRESSIVE MODELS; LINEAR-MODELS; EUCLIDEAN LIKELIHOOD; MOMENT RESTRICTIONS;
D O I
10.1016/j.jspi.2013.10.001
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We summarize advances in empirical likelihood (EL). for time series data. The EL formulation for independent data is briefly presented, which can apply for inference in special time series problems, reproducing the Wilks phenomenon of chi-square limits for log-ratio statistics. For more general inference with time series, versions of time domain block-based EL, and its generalizations based on divergence measures, are described along with their distributional properties; some approaches are intended for mixing time processes and others are tailored to time series with a Markovian structure. We also present frequency domain EL methods based on the periodogram. Finally, EL for long-range dependent processes is reviewed as well as recent advantages in EL for high dimensional problems. Some illustrative numerical examples are given along with a summary of open research issues for EL with dependent data. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:1 / 18
页数:18
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