Inference for Long-memory Time Series Models Based on Modified Empirical Likelihood

Authors

  • Ramadha D. Piyadi Gamage
  • Wei Ning Bowling Green State University

DOI:

https://doi.org/10.17713/ajs.v49i5.983

Abstract

Empirical likelihood method has been applied to short-memory time series models by Monti (1997) through the Whittle's estimation method. Yau (2012) extended this idea to long-memory time series models. Asymptotic distributions of the empirical likelihood ratio statistic for short and long-memory time series have been derived to construct confidence regions for the corresponding model parameters. However, it experiences the undercoverage issue which causes the coverage probabilities of parameters lower than the given nominal levels, especially for small sample sizes. In this paper, we propose a modified empirical likelihood which combines the advantages of the adjusted empirical likelihood and the transformed empirical likelihood to modify the one proposed by Yau (2012) for autoregressive fractionally integrated moving average (ARFIMA) model for the purpose of improving coverage probabilities.
Asymptotic null distribution of the test statistic has been established as the standard chi-square distribution with the degree of freedom one. Simulations have been conducted to investigate the performance of the proposed method as well as the comparisons of other existing methods to illustrate that the proposed method can provide better coverage probabilities especially for small sample sizes.

Published

2020-08-27

How to Cite

Gamage, R. D. P., & Ning, W. (2020). Inference for Long-memory Time Series Models Based on Modified Empirical Likelihood. Austrian Journal of Statistics, 49(5), 68-79. https://doi.org/10.17713/ajs.v49i5.983