Discrete Choice Methods with Simulation
Discrete Choice Methods with Simulation
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DOI：
10.1080/07474938.2014.975634
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发表时间：
2016-02
影响因子：
1.2
通讯作者：
Florian Heiss
中科院分区：
文献类型：
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作者：
Florian Heiss
Kenneth Train的《离散选择方法与模拟》自2009年起已出版第二版。这本书由剑桥大学出版社出版，也可以在作者的个人主页上下载。第二版纠正了一些错误，并包含两个额外的章节，一个关于内生回归和一个关于期望最大化（EM）算法。正如标题所示，本书有两个主要主题。一个是离散（多项）选择模型，处理有限的一组相互排斥的选择之间的决策，如消费品品牌，旅行方式或大学专业之间的选择。这些模型对于市场营销、运输、环境经济、教育、劳动和产业组织等领域都很重要。本书的第二个主要主题是基于模拟的估计，重点是最大模拟似然。这两个主题是非常兼容的，因为多项选择模型很自然地导致似然函数和其他目标函数，这些函数在分析上是不可行的，需要近似方法，如蒙特卡罗模拟。因此，许多基于模拟的估计的方法学工作受到离散选择模型的启发，而最近研究离散选择的许多实证工作都使用基于模拟的估计。
Discrete Choice Methods with Simulation by Kenneth Train has been available in the second edition since 2009. The book is published by Cambridge University Press and is also available for download on the author’s homepage for private use. The second edition corrects some errors and contains two additional chapters, one on endogenous regressors and one on the expectation–maximization (EM) algorithm. As the title suggests, the book has two main topics. One concerns models of discrete (multinomial) choices that deal with the decision between a finite set of mutually exclusive alternatives such as the choice between brands of consumer goods, travel modes, or college majors. These models are important for fields such as marketing, transportation, environmental economics, education, labor, and industrial organization. The second major subject of the book is a simulation-based estimation with a focus on maximum simulated likelihood. These two topics are very compatible since multinomial choice models quite naturally lead to likelihood functions and other objective functions that are analytically infeasible to evaluate, requiring approximation methods such as a Monte Carlo simulation. As a consequence, much of the methodological work on simulation-based estimation was inspired by discrete choice models and much of the more recent empirical work studying discrete choices uses simulation-based estimation.