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Likelihood Inference for Generalized Integer Autoregressive Time Series Models Joe, Harry

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.

Item Metadata

Title
Likelihood Inference for Generalized Integer Autoregressive Time Series Models
Creator
Joe, Harry
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2019-10-11
Description
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.
Subject
count time series; binomial thinning; thinning operators; compounding operation; self-generalized property
Genre
Article
Type
Text
Language
eng
Date Available
2020-01-08
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0388200
URI
http://hdl.handle.net/2429/73233
Affiliation
Science, Faculty of; Statistics, Department of
Citation
Econometrics 7 (4): 43 (2019)
Publisher DOI
10.3390/econometrics7040043
Peer Review Status
Reviewed
Scholarly Level
Faculty
Rights URI
https://creativecommons.org/licenses/by/4.0/
Aggregated Source Repository
DSpace

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Rights

CC BY 4.0