Conditional maximum likelihood estimation for a class of observation-driven time series models for count data
Yunwei Cui and
Qi Zheng
Statistics & Probability Letters, 2017, vol. 123, issue C, 193-201
Abstract:
This paper investigates the statistical inference for a class of observation-driven time series models of count data based on the conditional maximum likelihood estimator (CMLE), where the conditional distribution of the observed count given a state process is from the one-parameter exponential family. Under certain regularity conditions, the strong consistency and asymptotic normality of the CMLE of the misspecified likelihood function are established.
Keywords: Observation-driven models; One-parameter exponential family; INGARCH(p,q) models; Time series of counts (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715216302425
Full text for ScienceDirect subscribers only
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:eee:stapro:v:123:y:2017:i:c:p:193-201
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
DOI: 10.1016/j.spl.2016.11.002
Access Statistics for this article
Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul
More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().