Finite sample limited information inference methods for structural equations and models with generated regressors

• 成果类型: Article
• 署名作者: Dufour, JM; Jasiak, J
• 署名单位: Universite de Montreal; York University - Canada
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/1468-2354.00135
• 发表日期: 2001
• 页码: 815-843
• 关键词: instrumental variables regression; exact tests; confidence-intervals; parameters; estimators; relevance; 2-stage; BIAS; sets

摘要

We propose exact tests and confidence sets for various structural models typically estimated by IV methods, such as models with unobserved regressors, which remain valid despite the presence of identification problems or weak instruments. Two approaches are considered: (1) an instrument substitution method, which generalizes the Anderson-Rubin procedure. and (2) a sample-split method, that allows the use of generated regressors. Projection techniques are also proposed for inference on general parameter transformations. The asymptotic theory of the tests under weaker assumptions is discussed. Simulation results are presented. The suggested techniques are applied to a model of Tobin's q and to a model of academic performance.