Bernoulli Regression Models: Re-examining Statistical Models with Binary Dependent Variables
AbstractThe classical approach for specifying statistical models with binary dependent variables in econometrics using latent variables or threshold models can leave the model misspecified, resulting in biased and inconsistent estimates as well as erroneous inferences. Furthermore, methods for trying to alleviate such problems, such as univariate generalized linear models, have not provided an adequate alternative for ensuring the statistical adequacy of such models. The purpose of this paper is to re-examine the underlying probabilistic foundations of statistical models with binary dependent variables using the probabilistic reduction approach to provide an alternative approach for model specification. This re-examination leads to the development of the Bernoulli Regression Model. Simulated and empirical examples provide evidence that the Bernoulli Regression Model can provide a superior approach for specifying statistically adequate models for dichotomous choice processes.
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Bibliographic InfoPaper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2005 Annual meeting, July 24-27, Providence, RI with number 19282.
Date of creation: 2005
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Bernoulli Regression Model; logistic regression; generalized linear models; discrete choice; probabilistic reduction approach; model specification; Research Methods/ Statistical Methods;
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- Spanos,Aris, 1999. "Probability Theory and Statistical Inference," Cambridge Books, Cambridge University Press, number 9780521424080, December.
- Spanos, Aris, 1995. "On theory testing in econometrics : Modeling with nonexperimental data," Journal of Econometrics, Elsevier, vol. 67(1), pages 189-226, May.
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