Bayesian empirical likelihood inference and order shrinkage for autoregressive models

Kai Yang, Xue Ding and Xiaohui Yuan ()
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Kai Yang: Changchun University of Technology
Xue Ding: Changchun University of Technology
Xiaohui Yuan: Changchun University of Technology

Statistical Papers, 2022, vol. 63, issue 1, No 4, 97-121

Abstract: Abstract This paper considers the Bayesian empirical likelihood (BEL) inference and order shrinkage for a class of sparse autoregressive models without assuming the distributions for the errors. By introducing a nonparametric likelihood, parameters’ point and interval estimators, as well as some asymptotic properties of the estimators are obtained. By introducing a spike-and-slab prior, the order and the non-zero autoregressive coefficients of the model can be easily and accurately determined together via the Markov Chain Monte Carlo (MCMC) techniques. Simulation studies are conducted to evaluate the proposed methods. Finally, a real data example of the US industrial production index data set is applied to show the good performances of the BEL methods.

Keywords: Sparse autoregressive models; Bayesian empirical likelihood; Nonparametric Bayesian inference; Spike-and-slab prior; Order shrinkage (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00362-021-01231-6

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