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Scientific article
English

The Effect of Misclassifications in Probit Models. Monte Carlo Simulations and Applications

ContributorsHug, Simonorcid
Published inPolitical analysis, vol. 18, p. 78-102
Publication date2010
Abstract

The increased use of models with limited-dependent variables has allowed researchers to test important relationships in political science. Often, however, researchers employing such models fail to acknowledge that the violation of some basic assumptions has in part difference consequences in nonlinear models than in linear ones. In this paper I demonstrate this for binary probit models in which the dependent variable is systematically miscoded. Contrary to the linear model, such misclassifications affect not only the estimate of the intercept, but also those of the other coefficients. In a Monte-Carlo simulation I demonstrate that a model proposed by citeasnoun{Hausman1998} allows for correcting these biases in binary probit models. Empirical examples based on reanalyses of models explaining the occurrence of rebellions and civil wars demonstrate the problem that comes from neglecting these misclassifications

Keywords
  • Probit models
  • Measurement errors
  • Conflict research
Citation (ISO format)
HUG, Simon. The Effect of Misclassifications in Probit Models. Monte Carlo Simulations and Applications. In: Political analysis, 2010, vol. 18, p. 78–102.
Main files (1)
Article (Submitted version)
accessLevelRestricted
Identifiers
  • PID : unige:3957
ISSN of the journal1047-1987
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Technical informations

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