Economics at your fingertips  

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 Track citations by RSS feed

Downloads: (external link)
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:

Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01

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
Series data maintained by Dana Niculescu ().

Page updated 2017-09-29
Handle: RePEc:eee:stapro:v:123:y:2017:i:c:p:193-201