Monetary policy under model and data-parameter uncertainty

• 成果类型: Article
• 署名作者: Cateau, Gino
• 署名单位: Bank of Canada
• 刊物名称: JOURNAL OF MONETARY ECONOMICS
• ISSN/ISSBN: 0304-3932
• DOI: 10.1016/j.jmoneco.2006.11.004
• 发表日期: 2007
• 页码: 2083-2101
• 关键词: Taylor rule; uncertainty; non-reduction two-stage lotteries

摘要

Empirical Taylor rules are much less aggressive than those derived from optimization-based models. This paper analyzes whether accounting for uncertainty across competing models and (or) real-time data considerations can explain this discrepancy. It considers a central bank that chooses a Taylor rule in a framework that allows for an aversion to the second-order risk associated with facing multiple models and measurement-error configurations. The paper finds that if the central bank cares strongly enough about stabilizing the output gap, this aversion leads to significant declines in the coefficients of the Taylor rule even if the central bank's loss function assigns little weight to reducing interest rate variability. Furthermore, a small degree of aversion can generate an optimal rule that matches the empirical Taylor rule. (c) 2006 Elsevier B.V. All rights reserved.