Inference in models with adaptive learning

• 成果类型: Article
• 署名作者: Chevillon, Guillaume; Massmann, Michael; Mavroeidis, Sophocles
• 署名单位: Brown University; Vrije Universiteit Amsterdam; ESSEC Business School; Institut Polytechnique de Paris; ENSAE Paris
• 刊物名称: JOURNAL OF MONETARY ECONOMICS
• ISSN/ISSBN: 0304-3932
• DOI: 10.1016/j.jmoneco.2010.02.003
• 发表日期: 2010
• 页码: 341-351
• 关键词: Weak identification; persistence; Anderson-Rubin statistic; DSGE models

摘要

Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be conducted using the Anderson-Rubin statistic with appropriate choice of instruments. Application of this method to a typical new Keynesian sticky-price model with perpetual learning demonstrates its usefulness in practice. (C) 2010 Elsevier B.V. All rights reserved.