Arbitrarily normalized coefficients, information sets, and false reports of biases in binary outcome models

• 成果类型: Article
• 署名作者: Mroz, Thomas A.; Zayats, Yaraslau V.
• 署名单位: Clemson University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest.90.3.406
• 发表日期: 2008-08
• 页码: 406-413
• 关键词: specification error; duration dependence; multilevel models; approximations

摘要

Empirical researchers sometimes misinterpret how additional regressors, heterogeneity corrections, and multilevel factors impact the interpretation of the estimated parameters in binary outcome models such as logit and probit. This can result in incorrect inferences about the importance of incorporating such features in these nonlinear statistical models. Some reports of biases in binary outcome models appear related to the arbitrary variance normalization required in binary outcome models. A focus on readily interpretable numerical quantities, rather than conveniently chosen effects as measured by arbitrarily scaled coefficients, would eliminate nearly all of the interpretation problems we highlight in this paper.