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Variable selection in generalized random coefficient autoregressive models.

Zhiwen Zhao1, Yangping Liu1, Cuixin Peng2

  • 11College of Mathematics, Jilin Normal University, Siping, P.R. China.

Journal of Inequalities and Applications
|April 21, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for variable selection in generalized random coefficient autoregressive models (GRCA). It uses non-parametric empirical likelihood to develop novel information criteria for model selection.

Keywords:
Akaike information criterionBayesian information criterionEmpirical likelihoodGeneralized random coefficient autoregressive modelVariable selection

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Area of Science:

  • Statistics
  • Econometrics
  • Time Series Analysis

Background:

  • Variable selection is crucial for generalized random coefficient autoregressive (GRCA) models.
  • Traditional methods often rely on parametric likelihood, which can be restrictive.

Purpose of the Study:

  • To address the variable selection problem in GRCA models.
  • To introduce a non-parametric approach using empirical likelihood.

Main Methods:

  • Utilizing non-parametric empirical likelihood within an information-theoretic framework.
  • Developing empirical likelihood-based Akaike information criterion (AIC) and Bayesian information criterion (BIC).

Main Results:

  • Successfully adapted empirical likelihood for variable selection in GRCA models.
  • Proposed novel AIC and BIC criteria based on empirical likelihood.

Conclusions:

  • The proposed empirical likelihood-based criteria offer a viable alternative to parametric methods for GRCA variable selection.
  • This approach enhances model selection robustness in time series analysis.