1. The Accuracy of Linear and Nonlinear Estimation in the Presence of the Zero Lower Bound.
- Author
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Atkinson, Tyler, Richter, Alexander W., and Throckmorton, Nathaniel A.
- Subjects
LINEAR statistical models ,NONLINEAR statistical models ,KALMAN filtering ,ESTIMATION theory ,ALGORITHMS ,MONTE Carlo method - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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