The Zero Lower Bound and Estimation Accuracy
36 Pages Posted: 13 Jun 2018 Last revised: 29 Apr 2020
Date Written: May, 2018
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
JEL Classification: C11, C32, C51, E43
Suggested Citation: Suggested Citation