Solving, Estimating, and Selecting Nonlinear Dynamic Models Without the Curse of Dimensionality
AbstractWe present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids to overcome the curse of dimensionality for approximations. We apply sparse grids to a global polynomial approximation of the model solution, to the quadrature of integrals arising as rational expectations, and to three new nonlinear state space filters which speed up the sequential importance resampling particle filter. The posterior of the structural parameters is estimated by a new Metropolis-Hastings algorithm with mixing parallel sequences. The parallel extension improves the global maximization property of the algorithm, simplifies the parameterization for an appropriate acceptance ratio, and allows a simple implementation of the estimation on parallel computers. Finally, we provide all algorithms in the open source software JBendge for the solution and estimation of a general class of models. Copyright 2010 The Econometric Society.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Econometric Society in its journal Econometrica.
Volume (Year): 78 (2010)
Issue (Month): 2 (03)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.
- Creel, Michael & Kristensen, Dennis, 2011.
"Indirect Likelihood Inference,"
Dynare Working Papers
- Michael Creel & Dennis Kristensen, 2011. "Indirect likelihood inference," UFAE and IAE Working Papers 874.11, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
- Michael Creel & Dennis Kristensen, 2011. "Indirect likelihood inference," Working Papers 558, Barcelona Graduate School of Economics.
- Pichler, Paul, 2011. "Solving the multi-country Real Business Cycle model using a monomial rule Galerkin method," Journal of Economic Dynamics and Control, Elsevier, vol. 35(2), pages 240-251, February.
- Kenneth L. Judd & Lilia Maliar & Serguei Maliar & Rafael Valero, 2013.
"Smolyak Method for Solving Dynamic Economic Models: Lagrange Interpolation, Anisotropic Grid and Adaptive Domain,"
BYU Macroeconomics and Computational Laboratory Working Paper Series
2013-02, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.
- Kenneth L. Judd & Lilia Maliar & Serguei Maliar & Rafael Valero, 2013. "Smolyak Method for Solving Dynamic Economic Models: Lagrange Interpolation, Anisotropic Grid and Adaptive Domain," NBER Working Papers 19326, National Bureau of Economic Research, Inc.
- Kenneth Judd & Lilia Maliar & Rafael Valero & Serguei Maliar, 2013. "Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain," Working Papers. Serie AD 2013-06, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
- Christophe Gouel, 2013. "Comparing Numerical Methods for Solving the Competitive Storage Model," Computational Economics, Society for Computational Economics, vol. 41(2), pages 267-295, February.
- Luati, Alessandra & Proietti, Tommaso, 2012.
"Maximum likelihood estimation of time series models: the Kalman filter and beyond,"
02 BAWP, University of Sydney Business School, Discipline of Business Analytics.
- Tommaso, Proietti & Alessandra, Luati, 2012. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," MPRA Paper 39600, University Library of Munich, Germany.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.