The Zero Lower Bound and Estimation Accuracy
Tyler Atkinson (),
Alexander Richter () and
No 1804, Working Papers from Federal Reserve Bank of Dallas
During the Great Recession, many central banks lowered their policy rate to its zero lower bound (ZLB), creating a kink in the policy rule and calling into question linear estimation methods. There are two promising alternatives: estimate a fully nonlinear model that accounts for precautionary savings effects of the ZLB or a piecewise linear model that is much faster but ignores the precautionary savings effects. Repeated estimation with artificial datasets reveals some advantages of the nonlinear model, but they are not large enough to justify the longer estimation time, regardless of the ZLB duration in the data. Misspecification of the estimated models has a much larger impact on accuracy. It biases the parameter estimates and creates significant differences between the predictions of the models and the data generating process.
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)
Pages: 36 pages
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-mac and nep-ore
Note: A previous version of this paper circulated with the title, "The Accuracy of Linear and Nonlinear Estimation in the Presence of the Zero Lower Bound."
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https://www.dallasfed.org/-/media/documents/research/papers/2018/wp1804r1.pdf Revised version (application/pdf)
https://www.dallasfed.org/-/media/documents/research/papers/2018/wp1804.pdf Original version (application/pdf)
Journal Article: The zero lower bound and estimation accuracy (2020)
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