Parametric links for binary choice models: A Fisherian-Bayesian colloquy
Parametric links for binary choice models: A Fisherian-Bayesian colloquy
复制标题
DOI：
10.1016/j.jeconom.2009.01.009
复制
发表时间：
2009-10-01
影响因子：
6.3
通讯作者：
Yoon, Jungmo
中科院分区：
文献类型：
--
作者：
Koenker, Roger;Yoon, Jungmo
熟悉的logit和probit模型为许多二进制响应应用程序提供了方便的设置，但有时可能需要更大的链接函数类。研究了链路函数的两个参数族：基于Student t潜变量模型的Gosset链路，其自由度参数控制尾部行为；基于（广义）Tukey lambda族的Pregibon链路，其两个形状参数控制偏度和尾部行为。对贝叶斯和极大似然两种估计和推理方法进行了探讨、比较和对比。在应用程序中，如下面讨论的倾向得分匹配问题，其中对条件概率的准确估计至关重要，我们发现链接函数的错误说明可能会产生严重的偏差。基于MCMC的贝叶斯点估计与MLE方法相比具有很强的竞争力；然而，贝叶斯可信区域的名义覆盖范围更成问题。(C) 2009 Elsevier B.V.版权所有
The familiar logit and probit models provide convenient settings for many binary response applications, but a larger class of link functions may be occasionally desirable. Two parametric families of link functions are investigated: the Gosset link based on the Student t latent variable model with the degrees of freedom parameter controlling the tail behavior, and the Pregibon link based on the (generalized) Tukey lambda family, with two shape parameters controlling skewness and tail behavior. Both Bayesian and maximum likelihood methods for estimation and inference are explored, compared and contrasted. In applications, like the propensity score matching problem discussed below, where it is critical to have accurate estimates of the conditional probabilities, we find that misspecification of the link function can create serious bias. Bayesian point estimation via MCMC performs quite competitively with MLE methods; however nominal coverage of Bayes credible regions is somewhat more problematic. (C) 2009 Elsevier B.V. All rights reserved