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Provedor de dados:  AgEcon
País:  United States
Título:  Bernoulli Regression Models: Re-examining Statistical Models with Binary Dependent Variables
Autores:  Bergtold, Jason S.
Spanos, Aris
Data:  2005-05-16
Ano:  2005
Palavras-chave:  Bernoulli Regression Model
Logistic regression
Generalized linear models
Discrete choice
Probabilistic reduction approach
Model specification
Research Methods/ Statistical Methods
Resumo:  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.
Tipo:  Conference Paper or Presentation
Idioma:  Inglês
Identificador:  16234

http://purl.umn.edu/19282
Editor:  AgEcon Search
Relação:  American Agricultural Economics Association>2005 Annual meeting, July 24-27, Providence, RI
Selected Paper 136430
Formato:  35

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