What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

Markus Jochmann

Research output: Working paperDiscussion paper

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This paper develops stochastic search variable selection (SSVS) for zero-inflated
count models which are commonly used in health economics. This allows for
either model averaging or model selection in situations with many potential
regressors. The proposed techniques are applied to a data set from Germany
considering the demand for health care. A package for the free statistical software environment R is provided.
Original languageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Number of pages21
Publication statusPublished - Oct 2009


  • bayesian
  • model selection
  • model averaging
  • count data
  • zero-inflation
  • demand for health care

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