Non-linear DSGE Models and The Central Difference Kalman Filter
This paper introduces a Quasi Maximum Likelihood (QML) approach based on the Central Difference Kalman Filter (CDKF) to estimate non-linear DSGE models with potentially non-Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models solved up to third order. A Monte Carlo study shows that this QML estimator is basically unbiased and normally distributed infi?nite samples for DSGE models solved using a second order or a third order approximation. These results hold even when structural shocks are Gaussian, Laplace distributed, or display stochastic volatility.
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- Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez & Manuel Santos, 2004.
"Convergence Properties of the Likelihood of Computed Dynamic Models,"
PIER Working Paper Archive
04-034, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Jes�s Fernández-Villaverde & Juan F. Rubio-Ramírez & Manuel S. Santos, 2006. "Convergence Properties of the Likelihood of Computed Dynamic Models," Econometrica, Econometric Society, vol. 74(1), pages 93-119, 01.
- Jesus Fernandez-Villaverde & Juan Rubio & Manuel Santos, 2005. "Convergence Properties of the Likelihood of Computed Dynamic Models," NBER Technical Working Papers 0315, National Bureau of Economic Research, Inc.
- Jesús Fernández-Villaverde & Juan Francisco Rubio-Ramírez & Manuel S. Santos, 2004. "Convergence properties of the likelihood of computed dynamic models," FRB Atlanta Working Paper 2004-27, Federal Reserve Bank of Atlanta.
- Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Manuel Santos, 2005. "Convergence Properties of the Likelihood of Computed Dynamic Models," Levine's Bibliography 122247000000000822, UCLA Department of Economics.
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