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Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood Author info | Abstract | Publisher info | Download info | Related research | Statistics Jesus Fernandez-Villaverde () (Department of Economics, University of Pennsylvania)
Juan F. Rubio-Ramirez () (Federal Reserve Bank of Atlanta)
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This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a Sequential Monte Carlo filter proposed by Fernández-Villaverde and Rubio-Ramírez (2004) 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, even if 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.
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Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number
04-005.
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Length: 37 pages
Date of creation: 20 Jan 2004Date of revision:
Handle: RePEc:pen:papers:04-005Contact details of provider: Postal: 3718 Locust Walk, Philadelphia, PA 19104 Phone: 215-898-9992 Fax: 215-573-2378 Email: Web page: http://economics.sas.upenn.edu/pier More information through EDIRC
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Keywords: Likelihood-Based Inference ; Dynamic Equilibrium Economies ; Nonlinear Filtering ; Kalman Filter ; Sequential Monte Carlo ; Other versions of this item:
Find related papers by JEL classification: C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.: S. Boragan Aruoba & Jesus Fernandez-Villaverde & Juan Francisco Rubio-Ramirez, 2003.
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