文章基本信息

标题：Bernoulli Regression Models: Re-examining Statistical Models with Binary Dependent Variables
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作者：Bergtold, Jason S. ; Spanos, Aris
期刊名称：Journal of Agribusiness
印刷版ISSN：0738-8950
出版年度：2005
期号：suppl
出版社：Journal of Agribusiness
摘要：The 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.
关键词：Bernoulli Regression Model;logistic regression;generalized linear models;discrete choice;probabilistic reduction approach;model specification