## Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood (2005)

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Venue: | Journal of Applied Econometrics |

Citations: | 28 - 12 self |

### BibTeX

@ARTICLE{Fernández-villaverde05estimatingdynamic,

author = {Jesús Fernández-villaverde and Juan and F. Rubio-ram Írezb},

title = {Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood},

journal = {Journal of Applied Econometrics},

year = {2005},

volume = {20},

pages = {891--910}

}

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### Abstract

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. 1.

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Citation Context ...re. 1 It is important to note that we are presenting here only a basic sequential Monte Carlo filter and that the literature has presented several refinements to improve efficiency (see, for example, =-=Kitagawa, 1996-=-; Pitt and Shephard, 1999). The interested reader can find further details, comparison with alternative schemes and a discussion of convergence in Fernández-Villaverde and Rubio-Ramírez (2004). 2.3. T... |

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