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Are Nonlinear Methods Necessary at the Zero Lower Bound?
Throckmorton, Nathaniel Richter, Alexander W.
Throckmorton, Nathaniel
Richter, Alexander W.
Abstract
This paper examines the importance of using nonlinear methods to account for the zero lower bound (ZLB) on the Fed’s policy rate. We estimate three models with a particle filter: (1) a nonlinear model with a ZLB constraint; (2) a constrained linear model that imposes the constraint in the filter but not the solution; and (3) an unconstrained linear model that never imposes the constraint. The linear models have a lower likelihood than the nonlinear model
when the Fed is constrained and predict large monetary policy shocks during the ZLB period. We also compare the predictions from our nonlinear model to the quasi-linear solution with OccBin. OccBin captures the ZLB much better than the linear solutions but it still generates less endogenous volatility than the nonlinear model and it is not as conducive to estimation. Finally, we extend the baseline model to include a banking sector. We find larger differences between the predictions from the nonlinear model and both the linear and quasi-linear models.
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Date
2016
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Federal Reserve Bank of Dallas
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Keywords
Bayesian Estimation, Model comparison, Zero lower bound, particle filter