The Accuracy of Linear and Nonlinear Estimation in the Presence of the Zero Lower Bound
32 Pages Posted: 13 Jun 2018
Date Written: 2018-05-07
This paper evaluates the accuracy of linear and nonlinear estimation methods for dynamic stochastic general equilibrium models. We generate a large sample of artificial datasets using a global solution to a nonlinear New Keynesian model with an occasionally binding zero lower bound (ZLB) constraint on the nominal interest rate. For each dataset, we estimate the nonlinear model—solved globally, accounting for the ZLB—and the linear analogue of the nonlinear model—solved locally, ignoring the ZLB—with a Metropolis-Hastings algorithm where the likelihood function is evaluated with a Kalman filter, unscented Kalman filter, or particle filter. In datasets that resemble the U.S. experience, the nonlinear model estimated with a particle filter is more accurate and has a higher marginal data density than the linear model estimated with a Kalman filter, as long as the measurement error variances in the particle filter are not too big.
Keywords: Bayesian estimation, nonlinear solution, particle filter, unscented Kalman filter
JEL Classification: C11, C32, C51, E43
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