Two-Threshold-Variable Integer-Valued Autoregressive Model

Jiayue Zhang, Fukang Zhu () and Huaping Chen
Additional contact information
Jiayue Zhang: School of Mathematics, Jilin University, Changchun 130012, China
Fukang Zhu: School of Mathematics, Jilin University, Changchun 130012, China
Huaping Chen: School of Mathematics and Statistics, Henan University, Kaifeng 475004, China

Mathematics, 2023, vol. 11, issue 16, 1-20

Abstract: In the past, most threshold models considered a single threshold variable. However, for some practical applications, models with two threshold variables may be needed. In this paper, we propose a two-threshold-variable integer-valued autoregressive model based on the binomial thinning operator and discuss some of its basic properties, including the mean, variance, strict stationarity, and ergodicity. We consider the conditional least squares (CLS) estimation and discuss the asymptotic normality of the CLS estimator under the known and unknown threshold values. The performances of the CLS estimator are compared via simulation studies. In addition, two real data sets are considered to underline the superior performance of the proposed model.

Keywords: two-threshold-variable; time series of counts; integer-valued autoregression; conditional least squares estimate (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/16/3586/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/16/3586/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:16:p:3586-:d:1220439

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().