Likelihood Inference for Generalized Integer Autoregressive Time Series Models

Harry Joe
Additional contact information
Harry Joe: Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

Econometrics, 2019, vol. 7, issue 4, 1-13

Abstract: For modeling count time series data, one class of models is generalized integer autoregressive of order p based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an observation given the past p observations. Two data examples are included and show that thinning operators based on compounding can substantially improve the model fit compared with the commonly used binomial thinning operator.

Keywords: count time series; binomial thinning; thinning operators; compounding operation; self-generalized property (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2225-1146/7/4/43/pdf (application/pdf)
https://www.mdpi.com/2225-1146/7/4/43/ (text/html)

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:gam:jecnmx:v:7:y:2019:i:4:p:43-:d:275407

Access Statistics for this article

Econometrics is currently edited by Ms. Jasmine Liu

More articles in Econometrics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().