Identification of parameters in normal error component logit-mixture (NECLM) models
Identification of parameters in normal error component logit-mixture (NECLM) models
复制标题
正态误差分量 Logit 混合 (NECLM) 模型中参数的识别
DOI：
10.1002/jae.971
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发表时间：
2007
影响因子：
2.1
通讯作者：
D. Bolduc
中科院分区：
文献类型：
--
作者：
Joan L. Walker;M. Ben;D. Bolduc
尽管logit-mixture模型的基本结构已得到很好的理解，但重要的识别和规范化问题常常被忽视。本文讨论的问题，在logit混合模型包含正态分布的误差成分与替代品或巢的替代品（正态误差成分logit混合，或NECLM，模型）的参数识别。NECLM模型包括特殊情况，如无限制的，固定的协方差矩阵;替代特定的方差;嵌套和交叉嵌套结构;以及面板数据的一些应用。提出了一个一般框架，用于确定哪些参数被识别，以及什么规范化时施加指定NECLM模型。通常需要在层次或结构形式上指定和估计NECLM模型。这就排除了处理效用差异的可能性，否则会极大地简化标识和规范化过程。我们的研究结果表明，识别并不总是直观的;例如，在logit混合模型中存在的归一化问题并不存在于类似的probit模型中。为了识别和正确规范化NECLM，我们引入了“相等条件”，除了标准的顺序和排名条件。识别条件的工作，通过一些特殊情况下，我们的研究结果证明与经验的例子，使用合成和真实的数据。版权所有© 2007约翰威利父子有限公司。
Although the basic structure of logit-mixture models is well understood, important identification and normalization issues often get overlooked. This paper addresses issues related to the identification of parameters in logit-mixture models containing normally distributed error components associated with alternatives or nests of alternatives (normal error component logit mixture, or NECLM, models). NECLM models include special cases such as unrestricted, fixed covariance matrices; alternative-specific variances; nesting and cross-nesting structures; and some applications to panel data. A general framework is presented for determining which parameters are identified as well as what normalization to impose when specifying NECLM models. It is generally necessary to specify and estimate NECLM models at the levels, or structural, form. This precludes working with utility differences, which would otherwise greatly simplify the identification and normalization process. Our results show that identification is not always intuitive; for example, normalization issues present in logit-mixture models are not present in analogous probit models. To identify and properly normalize the NECLM, we introduce the 'equality condition', an addition to the standard order and rank conditions. The identifying conditions are worked through for a number of special cases, and our findings are demonstrated with empirical examples using both synthetic and real data. Copyright © 2007 John Wiley & Sons, Ltd.