On estimating finite mixtures of multivariate regression and simultaneous equation models

Kamel Jedidi, Venkatram Ramaswamy, Wayne S. DeSarbo, Michel Wedel

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

We propose a maximum likelihood framework for estimating finite mixtures of multivariate regression and simultaneous equation models with multiple endogenous variables. The proposed "semi-parametric" approach posits that the sample of endogenous observations arises from a finite mixture of components (or latent-classes) of unknown proportions with multiple structural relations implied by the specified model for each latent-class. We devise an Expectation-Maximization algorithm in a maximum likelihood framework to simultaneously estimate the class proportions, the class-specific structural parameters, and posterior probabilities of membership of each observation into each latent-class. The appropriate number of classes can be chosen using various information-theoretic heuristics. A data set entailing cross-sectional observations for a diverse sample of businesses is used to illustrate the proposed approach.

Original languageEnglish (US)
Pages (from-to)266-289
Number of pages24
JournalStructural Equation Modeling
Volume3
Issue number3
DOIs
StatePublished - Dec 1 1996

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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