Non-linear DSGE Models and The Central Difference Kalman Filter
AbstractThis paper introduces a Quasi Maximum Likelihood (QML) approach based on the Cen- tral 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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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2010-30.
Date of creation: 20 Jul 2010
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Web page: http://www.econ.au.dk/afn/
Non-linear filtering; Non-Gaussian shocks; Quasi Maximum Likelihood; Stochastic volatility; Third order perturbation.;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-08-28 (All new papers)
- NEP-CBA-2010-08-28 (Central Banking)
- NEP-DGE-2010-08-28 (Dynamic General Equilibrium)
- NEP-ECM-2010-08-28 (Econometrics)
- NEP-ETS-2010-08-28 (Econometric Time Series)
- NEP-ORE-2010-08-28 (Operations Research)
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- Andreasen, Martin, 2011. "An estimated DSGE model: explaining variation in term premia," Bank of England working papers 441, Bank of England.
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