Tractable Latent State Filtering for Non-Linear DSGE Models - Dallas Fed
نویسنده
چکیده
This paper develops a novel approach for estimating latent state variables of Dynamic Stochastic General Equilibrium (DSGE) models that are solved using a second-order accurate approximation. I apply the Kalman filter to a state-space representation of the second-order solution based on the ‘pruning’ scheme of Kim, Kim, Schaumburg and Sims (2008). By contrast to particle filters, no stochastic simulations are needed for the filter here--the present method is thus much faster. In Monte Carlo experiments, the filter here generates more accurate estimates of latent state variables than the standard particle filter. The present filter is also more accurate than a conventional Kalman filter that treats the linearized model as the true data generating process. Due to its high speed, the filter presented here is suited for the estimation of model parameters; a quasi-maximum likelihood procedure can be used for that purpose. JEL codes: C63, C68, E37 * Robert Kollmann, European Centre for Advanced Research in Economics and Statistics (ECARES), CP 114, Université Libre de Bruxelles, 50 Av. Franklin Roosevelt, B-1050 Brussels, Belgium. 32-2-6504474. [email protected]. www.robertkollmann.com. I thank Raf Wouters for useful discussions. Financial support from the National Bank of Belgium and from 'Action de recherche concertée' ARC-AUWB/2010-15/ULB-11 is gratefully acknowledged. The views in this paper are those of the author and do not necessarily reflect the views of the Federal Reserve Bank of Dallas or the Federal Reserve System.
منابع مشابه
Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation
Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation* This paper develops a novel approach for estimating latent state variables of Dynamic Stochastic General Equilibrium (DSGE) models that are solved using a second-order accurate approximation. I apply the Kalman filter to a statespace representation of the second-order solution based on the ‘pruning’ ...
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