Penalized estimation of finite mixture models

IF 9.9 3区 经济学 Q1 ECONOMICS
Sofya Budanova
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引用次数: 0

Abstract

Economists often model unobserved heterogeneity using finite mixtures. In practice, the number of mixture components is rarely known. Model parameters lack point-identification if the estimation includes too many components, thus invalidating the classic properties of maximum likelihood estimation. I propose a penalized likelihood method to estimate finite mixtures with an unknown number of components. The resulting Order-Selection-Consistent Estimator (OSCE) consistently estimates the true number of components and achieves oracle efficiency. This paper extends penalized estimation to models without point-identification and to mixtures with growing number of components. I apply the OSCE to estimate players’ rationality levels in a coordination game.
有限混合模型的惩罚估计
经济学家经常使用有限混合模型来模拟未观察到的异质性。在实践中,混合成分的数量很少为人所知。如果估计包含太多的分量,则模型参数缺乏点识别,从而使最大似然估计的经典特性失效。我提出了一种惩罚似然法来估计具有未知数量成分的有限混合物。由此产生的Order-Selection-Consistent Estimator (OSCE)一致地估计组件的真实数量并实现oracle效率。本文将惩罚估计扩展到没有点识别的模型和具有越来越多成分的混合模型。我运用OSCE来估计协调博弈中玩家的理性水平。
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来源期刊
Journal of Econometrics
Journal of Econometrics 社会科学-数学跨学科应用
CiteScore
8.60
自引率
1.60%
发文量
220
审稿时长
3-8 weeks
期刊介绍: The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.
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