Estimating dynamic equilibrium economies: linear versus nonlinear likelihood
Article first published online: 19 DEC 2005
Copyright © 2005 John Wiley & Sons, Ltd.
Journal of Applied Econometrics
Volume 20, Issue 7, pages 891–910, December 2005
How to Cite
Fernández-Villaverde, J. and Rubio-Ramírez, J. F. (2005), Estimating dynamic equilibrium economies: linear versus nonlinear likelihood. J. Appl. Econ., 20: 891–910. doi: 10.1002/jae.814
- Issue published online: 19 DEC 2005
- Article first published online: 19 DEC 2005
- Manuscript Revised: 24 JUN 2004
- Manuscript Received: 20 JAN 2004
This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a sequential Monte Carlo filter and the Kalman filter. The sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. We report two main results. First, both for simulated and for real data, the sequential Monte Carlo filter delivers a substantially better fit of the model to the data as measured by the marginal likelihood. This is true even for a nearly linear case. Second, the differences in terms of point estimates, although relatively small in absolute values, have important effects on the moments of the model. We conclude that the nonlinear filter is a superior procedure for taking models to the data. Copyright © 2005 John Wiley & Sons, Ltd.