Estimating dynamic equilibrium economies: linear versus nonlinear likelihood

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...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2005-12, Vol.20 (7), p.891-910
Main Authors: Fernández-Villaverde, Jesús, Rubio-Ramírez, Juan F.
Format: Article
Language:English
Subjects:
Applied economics
Comparative studies
Dynamic models
Econometric models
Econometrics
Economic dynamics
Economic equilibrium
Economic modeling
Economic models
Equilibrium models
Kalman filters
Macroeconomic modeling
Mathematical methods
Maximum likelihood method
Modeling
Monte Carlo simulation
Neoclassical economics
Point estimators
Simulations
Steady state economies
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Summary: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.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.814