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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 \u00a9 2005 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.814","type":"journal-article","created":{"date-parts":[[2005,12,19]],"date-time":"2005-12-19T12:46:43Z","timestamp":1134996403000},"page":"891-910","source":"Crossref","is-referenced-by-count":106,"title":["Estimating dynamic equilibrium economies: linear versus nonlinear likelihood"],"prefix":"10.1002","volume":"20","author":[{"given":"Jes\u00fas","family":"Fern\u00e1ndez\u2010Villaverde","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan F.","family":"Rubio\u2010Ram\u00edrez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2005,12,19]]},"reference":[{"key":"e_1_2_8_2_1","volume-title":"Handbook of Computational Economics","author":"Anderson EW","year":"1996"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"AruobaSB Fern\u00e1ndez\u2010VillaverdeJ Rubio\u2010Ram\u00edrezJ.2003.Comparing solution methods for dynamic equilibrium economies. 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