The zero lower bound and estimation accuracy

• 成果类型: Article
• 署名作者: Atkinson, Tyler; Richter, Alexander W.; Throckmorton, Nathaniel A.
• 署名单位: Federal Reserve System - USA; Federal Reserve Bank - Dallas
• 刊物名称: JOURNAL OF MONETARY ECONOMICS
• ISSN/ISSBN: 0304-3932
• DOI: 10.1016/j.jmoneco.2019.06.007
• 发表日期: 2020
• 页码: 249-264
• 关键词: Bayesian estimation; projection methods; particle filter; Occbin; Inversion filter

摘要

During the Great Recession, central banks lowered their policy rate to the zero lower bound (ZLB), calling into question linear estimation methods. There are two alternatives: estimate a nonlinear model that accounts for precautionary savings effects of the ZLB or a piecewise linear model that is faster but ignores the precautionary savings effects. This paper compares their accuracy using artificial datasets. The predictions of the nonlinear model are typically more accurate than the piecewise linear model, but the differences are usually small. There are far larger gains in accuracy from estimating a richer, less misspecified piecewise linear model. (c) 2019 Elsevier B.V. All rights reserved.