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DSGE Model Estimation on the Basis of Second-Order Approximation

Listed author(s):
  • Sergey Ivashchenko

    ()

This article compares the properties of different non-linear Kalman filters: the well-known Unscented Kalman filter (UKF), the central difference Kalman filter (CDKF) and the new Quadratic Kalman filter (QKF). A small financial DSGE model is repeatedly estimated by several quasi-likelihood methods with different filters for data generated by the model. Errors in parameters estimation are a measure of the filters’ quality. The result shows that the QKF has a reasonable advantage in terms of quality over the CDKF and the UKF, albeit with some loss in speed. Copyright Springer Science+Business Media New York 2014

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File URL: http://hdl.handle.net/10.1007/s10614-013-9363-1
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Article provided by Springer & Society for Computational Economics in its journal Computational Economics.

Volume (Year): 43 (2014)
Issue (Month): 1 (January)
Pages: 71-82

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Handle: RePEc:kap:compec:v:43:y:2014:i:1:p:71-82
DOI: 10.1007/s10614-013-9363-1
Contact details of provider: Web page: http://www.springer.com

Web page: http://www.comp-econ.org/

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Order Information: Web: http://www.springer.com/economics/economic+theory/journal/10614/PS2

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  1. Tovar, Camilo Ernesto, 2009. "DSGE Models and Central Banks," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 3, pages 1-31.
  2. Collard, Fabrice & Juillard, Michel, 2001. "Accuracy of stochastic perturbation methods: The case of asset pricing models," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 979-999, June.
  3. Martin Møller Andreasen, 2008. "Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter," CREATES Research Papers 2008-33, Department of Economics and Business Economics, Aarhus University.
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