Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/100725 
Year of Publication: 
2004
Series/Report no.: 
Working Paper No. 2004-3
Publisher: 
Federal Reserve Bank of Atlanta, Atlanta, GA
Abstract: 
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. The authors 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. The authors conclude that the nonlinear filter is a superior procedure for taking models to the data.
Document Type: 
Working Paper

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