ENTROPIC LATENT VARIABLE INTEGRATION VIA SIMULATION

• 成果类型: Article
• 署名作者: Schennach, Susanne M.
• 署名单位: Brown University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA9748
• 发表日期: 2014
• 页码: 345-385
• 关键词: TESTING MOMENT RESTRICTIONS; PARTIALLY IDENTIFIED MODELS; Empirical Likelihood; PROBABILITY-DISTRIBUTIONS; Asymptotic Optimality; confidence-regions; econometric-models; generalized-method; MAXIMUM-ENTROPY; INTERVAL DATA

摘要

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.