A New Estimator for Multivariate Binary Data

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.


Issue Date:
2015
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/204963
JEL Codes:
B23; Q13; D1
Series Statement:
#6570




 Record created 2017-04-01, last modified 2017-08-28

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