Entropic Latent Variable Integration via Simulation
Entropic Latent Variable Integration via Simulation
复制标题
通过模拟进行熵潜变量积分
DOI：
10.3982/ecta9748
复制
发表时间：
2013
期刊：
影响因子：
6.1
通讯作者：
Susanne M. Schennach
中科院分区：
文献类型：
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作者：
Susanne M. Schennach
本文介绍了一种通用方法，将由涉及观测变量和未观测变量的矩条件定义的模型转换为仅涉及可观测变量的等效矩条件。使用最不利的熵最大化分布可以在不引入无限维干扰参数的情况下完成此任务。我们通过示例和模拟证明，这种方法涵盖了广泛的潜在变量模型，包括一些博弈论模型和具有有限因变量、区间值数据、变量误差或其组合的模型。点识别模型和集合识别模型都被透明地覆盖。在后一种情况下，该方法还通过提供为多种模型构造矩型目标函数的广义方法所需的矩条件来补充最近关于通用集合推理方法的文献。还给出了该方法的扩展，包括条件矩、独立性限制和一些状态空间模型。
This paper introduces a general method to convert a model defined by moment conditions that involve both observed and unobserved variables into equivalent moment conditions that involve only observable variables. This task can be accomplished without introducing infinite‐dimensional nuisance parameters using a least favorable entropy‐maximizing distribution. We demonstrate, through examples and simulations, that this approach covers a wide class of latent variables models, including some game‐theoretic models and models with limited dependent variables, interval‐valued data, errors‐in‐variables, or combinations thereof. Both point‐ and set‐identified models are transparently covered. In the latter case, the method also complements the recent literature on generic set‐inference methods by providing the moment conditions needed to construct a generalized method of moments‐type objective function for a wide class of models. Extensions of the method that cover conditional moments, independence restrictions, and some state‐space models are also given.