A pth-order random coefficients mixed binomial autoregressive process with explanatory variables

Han Li, Zijian Liu, Kai Yang (), Xiaogang Dong and Wenshan Wang
Additional contact information
Han Li: Changchun University
Zijian Liu: Changchun University of Technology
Kai Yang: Changchun University of Technology
Xiaogang Dong: Changchun University of Technology
Wenshan Wang: Changchun University of Technology

Computational Statistics, 2024, vol. 39, issue 5, No 7, 2604 pages

Abstract: Abstract To capture the higher-order autocorrelation structure for finite-range integer-valued time series of counts, and to consider the driving effect of covariates on the underlying process, this paper introduces a pth-order random coefficients mixed binomial autoregressive process with explanatory variables. The basic probabilistic and statistical properties of the model are discussed. Conditional least squares and conditional maximum likelihood estimators, as well as their asymptotic properties of the estimators are obtained. Moreover, the existence test of explanatory variables are well addressed using a Wald-type test. Forecasting problem is also considered. Finally, some numerical results of the estimators and a real data example are presented to show the performance of the proposed model.

Keywords: Finite-range integer-valued time series; Binomial autoregressive model; Random coefficient model; Explanatory variables; Forecasting (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-023-01396-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:compst:v:39:y:2024:i:5:d:10.1007_s00180-023-01396-8

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-023-01396-8

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().