Integer†Valued Autoregressive Models With Survival Probability Driven By A Stochastic Recurrence Equation

Paolo Gorgi

Journal of Time Series Analysis, 2018, vol. 39, issue 2, 150-171

Abstract: This paper proposes a new class of integer†valued autoregressive models with a dynamic survival probability. The peculiarity of this class of models lies in the specification of the survival probability through a stochastic recurrence equation. The proposed models can effectively capture changing dependence over time and enhance both the in†sample and out†of†sample performance of integer†valued autoregressive models. This point is illustrated through an empirical application to a real†time series of crime reports. Additionally, this paper discusses the reliability of likelihood†based inference for the class of models. In particular, this study proves the consistency of the maximum likelihood estimator and a plug†in estimator for the conditional probability mass function in a misspecified model setting.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://doi.org/10.1111/jtsa.12272

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:bla:jtsera:v:39:y:2018:i:2:p:150-171

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().