Mixed INAR(1) Poisson regression models: Analyzing heterogeneity and serial dependencies in longitudinal count data

Ulf Böckenholt*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

This paper presents finite mixture versions of integer-valued autoregressive (INAR) Poisson regression models for investigating regularity and predictability of purchase behavior over time. The approach facilitates the analysis of heterogeneity and serial correlation effects as well as conditional and marginal analyses of the effects of covariates. An application to scanner panel data of detergents yields substantive insights into sources of autodependencies in individual category purchases.

Original languageEnglish (US)
Pages (from-to)317-338
Number of pages22
JournalJournal of Econometrics
Volume89
Issue number1-2
DOIs
StatePublished - Nov 26 1998

Keywords

  • Autoregression
  • Binomial thinning
  • Count data
  • Finite mixture

ASJC Scopus subject areas

  • Economics and Econometrics

Fingerprint Dive into the research topics of 'Mixed INAR(1) Poisson regression models: Analyzing heterogeneity and serial dependencies in longitudinal count data'. Together they form a unique fingerprint.

Cite this