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A New Estimator for Multivariate Binary Data

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  • Fu, Shengfei
  • Shonkwiler, John Scott

Abstract

This study proposes a new estimator for multivariate binary response data. This study considers binary responses as being generated from a truncated multivariate discrete distribution. Specifically the discrete normal probability mass function, which has support on all integers, is extended to a multivariate form. Truncating this point probability mass function below zero and above one results the multivariate binary discrete normal distribution. This distribution has a number of attractive properties. Monte Carlo simulation and empirical applications are performed to show the properties of this new estimator; comparisons are made to the traditional multivariate probit model.

Suggested Citation

  • Fu, Shengfei & Shonkwiler, John Scott, 2015. "A New Estimator for Multivariate Binary Data," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 204963, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea15:204963
    DOI: 10.22004/ag.econ.204963
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    References listed on IDEAS

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