## 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 language | English (US) |
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Pages (from-to) | 266-289 |

Number of pages | 24 |

Journal | Structural Equation Modeling |

Volume | 3 |

Issue number | 3 |

DOIs | |

State | Published - Dec 1 1996 |

## All Science Journal Classification (ASJC) codes

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