IMPROVED JIVE ESTIMATORS FOR OVERIDENTIFIED LINEAR MODELS WITH AND WITHOUT HETEROSKEDASTICITY

• 成果类型: Article
• 署名作者: Ackerberg, Daniel A.; Devereux, Paul J.
• 署名单位: University of California System; University of California Los Angeles; Trinity College Dublin; University College Dublin
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest.91.2.351
• 发表日期: 2009-05
• 页码: 351-362
• 关键词: empirical likelihood; sample properties; number; BIAS

摘要

We introduce two simple new variants of the jackknife instrumental variables (JIVE) estimator for overidentified linear models and show that they are superior to the existing JIVE estimator, significantly improving on its small-sample-bias properties. We also compare our new estimators to existing Nagar (1959) type estimators. We show that, in models with heteroskedasticity, our estimators have superior properties to both the Nagar estimator and the related B2SLS estimator suggested in Donald and Newey (2001). These theoretical results are verified in a set of Monte Carlo experiments and then applied to estimating the returns to schooling using actual data.