Estimating nonlinear DSGE models with moments based methods
This article suggests the new approach to an approximation of nonlinear DSGE models moments. This approach is fast and accurate enough to use it for an estimation of nonlinear DSGE models. The small financial DSGE model is repeatedly estimated by several modifications of suggested approach. Approximations of moments are close to the results of large sample Monte Carlo estimation. Quality of parameters estimation with suggested approach is close to the Central Difference Kalman Filter (the CDKF) based. At the same time suggested approach is much faster.
|Date of creation:||Jan 2014|
|Date of revision:|
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- Collard, Fabrice & Juillard, Michel, 2001.
"Accuracy of stochastic perturbation methods: The case of asset pricing models,"
Journal of Economic Dynamics and Control,
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- Sungbae An & Frank Schorfheide, 2007.
"Bayesian Analysis of DSGE Models,"
Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
- 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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