The zero lower bound and estimation accuracy

Tyler Atkinson, Alexander Richter and Nathaniel Throckmorton

Journal of Monetary Economics, 2020, vol. 115, issue C, 249-264

Abstract: 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.

Keywords: Bayesian estimation; Projection methods; Particle filter; Occbin; Inversion filter (search for similar items in EconPapers)
JEL-codes: C11 C32 C51 E43 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Related works:
Working Paper: The Zero Lower Bound and Estimation Accuracy (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:moneco:v:115:y:2020:i:c:p:249-264

DOI: 10.1016/j.jmoneco.2019.06.007

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