Valid Inference in Partially Unstable Generalized Method of Moments Models

• 成果类型: Article
• 署名作者: Li, Hong; Mueller, Ulrich K.
• 署名单位: Brandeis University; Princeton University
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1111/j.1467-937X.2008.00516.x
• 发表日期: 2009
• 页码: 343-365
• 关键词: parameter instability; structural stability; optimal tests; heteroskedasticity

摘要

This paper considers time series Generalized Method of Moments (GMM) models where a subset of the parameters are time varying. We focus on an empirically relevant case with moderately large instabilities, which are well approximated by a local asymptotic embedding that does not allow the instability to be detected with certainty, even in the limit. We show that for many forms of the instability and a large class of GMM models, usual GMM inference on the subset of stable parameters is asymptotically unaffected by the partial instability. In the empirical analysis of presumably stable parameters-such as structural parameters in Euler conditions-one can thus ignore moderate instabilities in other parts of the model and still obtain approximately correct inference.