Empirical likelihood inference for a class of hysteretic autoregressive models

In this article, we consider empirical likelihood (EL) inference for a class of hysteretic autoregressive models. The main focus of this article is to use the EL method to construct confidence intervals for the parameters and derive the maximum empirical likelihood estimators (MELE) and their asymptotic properties under the conditions that the threshold variable is known or not. Additionally, the testing problem of the nonlinearity of the data is addressed. To illustrate the advantages of solving this model with EL method, we made a simulation study and empirical analysis on the data set of the unemployment rate.

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Publication Details

Journal
Communication in Statistics- Theory and Methods
Published
2024-10-03
DOI
https://doi.org/10.1080/03610926.2024.2397557
Citations
1
Primary Topic
Statistical Methods and Inference
Type
article
Field-Weighted Citation Impact
0.71

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article

Empirical likelihood inference for a class of hysteretic autoregressive models

Kai Yang, Guichen Han
1 citations
Communication in Statistics- Theory and Methods
Statistical Methods and Inference
0.71
article

Empirical likelihood inference for a class of hysteretic autoregressive models

Kai Yang, Guichen Han
article en
1 citations

Abstract

In this article, we consider empirical likelihood (EL) inference for a class of hysteretic autoregressive models. The main focus of this article is to use the EL method to construct confidence intervals for the parameters and derive the maximum empirical likelihood estimators (MELE) and their asymptotic properties under the conditions that the threshold variable is known or not. Additionally, the testing problem of the nonlinearity of the data is addressed. To illustrate the advantages of solving this model with EL method, we made a simulation study and empirical analysis on the data set of the unemployment rate.

Communication in Statistics- Theory and MethodsVol. 54(12)
Changchun University of Technology (CN)
Natural Science Foundation of Jilin Province, Jilin Provincial Postdoctoral Science Foundation, National Natural Science Foundation of China
Reduced inequalities
Openalex Percentile: Top 26%
Statistical Methods and Inference
0.71
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