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Updated: Jan 28, 2026

Basics of Multivariate Analysis in Neuroimaging Data
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Marginal Effects in Multivariate Probit Models.

John Mullahy1

  • 1University of Wisconsin-Madison, NUI Galway, and NBER, jmullahy@wisc.edu, +1-608-265-5410 (phone).

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|March 1, 2019
PubMed
Summary
This summary is machine-generated.

This study extends partial effects estimation for multivariate probit (MVP) models. It provides methods for analyzing complex outcomes in econometrics, enhancing understanding of covariate impacts.

Keywords:
C30C35marginal effectsmultivariate probit

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Area of Science:

  • Econometrics
  • Statistical Modeling

Background:

  • Applied microeconometric analysis often focuses on estimating partial effects of covariates on conditional parameters.
  • Partial effects estimation is well-established for univariate probit models and for quadrant probabilities in bivariate probit models.

Purpose of the Study:

  • To extend existing results for partial effects estimation to the general M≥2 multivariate probit (MVP) context.
  • To generalize these methods for arbitrary orthant probabilities, models conditioning on subvectors of outcomes, and multivariate ordered probit data structures.

Main Methods:

  • Extends Greene's results for partial effects estimation.
  • Applies methods to multivariate probit (MVP) models with M≥2 outcomes.
  • Adapts estimation for arbitrary orthant probabilities and conditional models.

Main Results:

  • Provides a framework for estimating partial effects in general multivariate probit models.
  • Extends the analysis to multivariate ordered probit models.
  • Demonstrates the utility of these partial effects for understanding multivariate outcomes.

Conclusions:

  • The developed methods offer a comprehensive approach to partial effects estimation in complex multivariate probit settings.
  • These techniques are broadly applicable and useful in microeconometric analyses involving multiple, jointly dependent outcomes.