Poisson autoregressive process modeling via the penalized conditional maximum likelihood procedure

Xinyang Wang, Dehui Wang and Haixiang Zhang
Additional contact information
Xinyang Wang: Mathematics School of Jilin University
Dehui Wang: Mathematics School of Jilin University
Haixiang Zhang: Tianjin University

Statistical Papers, 2020, vol. 61, issue 1, No 13, 245-260

Abstract: Abstract In this paper, we consider the penalized estimation procedure for Poisson autoregressive model with sparse parameter structure. We study the theoretical properties of penalized conditional maximum likelihood (PCML) with several different penalties. We show that the penalized estimators perform as well as the true model was known. We establish the oracle properties of PCML estimators. Some simulation studies are conducted to verify the proposed procedure. A real data example is also provided.

Keywords: Integer-valued time series; Penalty function; Poisson autoregressive; Oracle properties (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-017-0938-0 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:stpapr:v:61:y:2020:i:1:d:10.1007_s00362-017-0938-0

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-017-0938-0

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

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