Logo of Humboldt-Universität zu BerlinLogo of Humboldt-Universität zu Berlin
edoc-Server
Open-Access-Publikationsserver der Humboldt-Universität
de|en
Header image: facade of Humboldt-Universität zu Berlin
View Item 
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
View Item 
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
2008-04-25Buch DOI: 10.18452/4126
JBendge
An Object-Oriented System for Solving, Estimating and Selecting Nonlinear Dynamic Models
Winschel, Viktor
Krätzig, Markus
We present an object-oriented software framework allowing to specify, solve, and estimate nonlinear dynamic general equilibrium (DSGE) models. The imple- mented solution methods for finding the unknown policy function are the standard linearization around the deterministic steady state, and a function iterator using a multivariate global Chebyshev polynomial approximation with the Smolyak op- erator to overcome the course of dimensionality. The operator is also useful for numerical integration and we use it for the integrals arising in rational expecta- tions and in nonlinear state space filters. The estimation step is done by a parallel Metropolis-Hastings (MH) algorithm, using a linear or nonlinear filter. Implemented are the Kalman, Extended Kalman, Particle, Smolyak Kalman, Smolyak Sum, and Smolyak Kalman Particle filters. The MH sampling step can be interactively moni- tored and controlled by sequence and statistics plots. The number of parallel threads can be adjusted to benefit from multiprocessor environments. JBendge is based on the framework JStatCom, which provides a standardized ap- plication interface. All tasks are supported by an elaborate multi-threaded graphical user interface (GUI) with project management and data handling facilities.
Files in this item
Thumbnail
34.pdf — Adobe PDF — 935.9 Kb
MD5: 3ae3baace73aec2013785285b26c482a
Cite
BibTeX
EndNote
RIS
InCopyright
Details
DINI-Zertifikat 2019OpenAIRE validatedORCID Consortium
Imprint Policy Contact Data Privacy Statement
A service of University Library and Computer and Media Service
© Humboldt-Universität zu Berlin
 
DOI
10.18452/4126
Permanent URL
https://doi.org/10.18452/4126
HTML
<a href="https://doi.org/10.18452/4126">https://doi.org/10.18452/4126</a>