An INAR(1) negative multinomial regression model for longitudinal count data

Ulf Böckenholt*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


This paper discusses a regression model for the analysis of longitudinal count data observed in a panel study. An integer-valued first-order autoregressive [INAR(1)] Poisson process is adapted to represent time-dependent correlations among the counts. By combining the INAR(1)-representation with a random effects approach, a new negative multinomial distribution is derived that includes the bivariate negative binomial distribution proposed by Edwards and Gurland (1961) and Subrahmaniam (1966) as a special case. A detailed analysis of the relationship between personality factors and daily emotion experiences illustrates the approach.

Original languageEnglish (US)
Pages (from-to)53-67
Number of pages15
Issue number1
StatePublished - Mar 1999


  • Binomial thinning
  • Markov models
  • Poisson distribution

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics


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