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Estimating dynamic equilibrium economies: linear versus nonlinear likelihood (2005)
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| Venue: | Journal of Applied Econometrics |
| Citations: | 42 - 13 self |
BibTeX
@ARTICLE{Fernández-villaverde05estimatingdynamic,
author = {Jesús Fernández-villaverde and Juan F. Rubio-ramírez and Thank Sally Burke and Will Roberds and Chris Sims and Tao Zha},
title = {Estimating dynamic equilibrium economies: linear versus nonlinear likelihood},
journal = {Journal of Applied Econometrics},
year = {2005},
pages = {891--910}
}
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Abstract
This paper compares twomethods 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 meth-ods. 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.
Keyphrases
dynamic equilibrium economy sequential monte carlo filter linear versus nonlinear likelihood kalman filter absolute value nonlinear structure superior procedure likelihood-based inference real data important effect main result linear case steady state simulation meth-ods point estimate nonlinear filter likelihood function marginal likelihood
