Variable selection in generalized random coefficient autoregressive models

Zhiwen Zhao; Yangping Liu; Cuixin Peng

doi:10.1186/s13660-018-1680-4

Variable selection in generalized random coefficient autoregressive models

J Inequal Appl. 2018;2018(1):82. doi: 10.1186/s13660-018-1680-4. Epub 2018 Apr 12.

Authors

Zhiwen Zhao¹, Yangping Liu¹, Cuixin Peng²

Affiliations

¹ 1College of Mathematics, Jilin Normal University, Siping, P.R. China.
² 2Public Foreign Languages Department, Jilin Normal University, Siping, P.R. China.

Abstract

In this paper, we consider the variable selection problem of the generalized random coefficient autoregressive model (GRCA). Instead of parametric likelihood, we use non-parametric empirical likelihood in the information theoretic approach. We propose an empirical likelihood-based Akaike information criterion (AIC) and a Bayesian information criterion (BIC).

Keywords: Akaike information criterion; Bayesian information criterion; Empirical likelihood; Generalized random coefficient autoregressive model; Variable selection.