Efficient estimation in models with independence restrictions

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• 作者：Alexandre Poirier ^{alexandre-poirier@uiowa.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：C13 ; C14
• 刊名：Journal of Econometrics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：196
• 期：1
• 页码：1-22
• 全文大小：638 K

文摘

Unconditional and conditional independence restrictions are used in many econometric models to identify their parameters. However, there are few results about efficient estimation procedures for finite-dimensional parameters under these independence restrictions. This paper computes the efficiency bound for finite-dimensional parameters under independence restrictions, and proposes an estimator that is consistent, asymptotically normal and which achieves the efficiency bound. The estimator is based on a growing number of zero-covariance conditions that are asymptotically equivalent to the independence restriction. The results are illustrated with examples, including an instrumental variables regression model and partially linear regression models. A small Monte Carlo study is performed to investigate the estimator’s small sample properties and to quantify the efficiency gains that can be made by using the proposed efficient estimator.