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1.
Solving, Estimating and Selecting Nonlinear Dynamic Economic Models without the Curse of Dimensionality
Title:
Solving, Estimating and Selecting Nonlinear Dynamic Economic Models without the Curse of Dimensionality
Author:
Viktor Winschel
Viktor Winschel
Minimize authors
Description:
A welfare analysis of a risky policy is impossible within a linear or linearized model and its certainty equivalence property. The presented algorithms are designed as a toolbox for a general model class. The computational challenges are considerable and I concentrate on the numerics and statistics for a simple model of dynamic consumption and l...
A welfare analysis of a risky policy is impossible within a linear or linearized model and its certainty equivalence property. The presented algorithms are designed as a toolbox for a general model class. The computational challenges are considerable and I concentrate on the numerics and statistics for a simple model of dynamic consumption and labor choice. I calculate the optimal policy and estimate the posterior density of structural parameters and the marginal likelihood within a nonlinear state space model. My approach is even in an interpreted language twenty time faster than the only alternative compiled approach. The model is estimated on simulated data in order to test the routines against known true parameters. The policy function is approximated by Smolyak Chebyshev polynomials and the rational expectation integral by Smolyak Gaussian quadrature. The Smolyak operator is used to extend univariate approximation and integration operators to many dimensions. It reduces the curse of dimensionality from exponential to polynomial growth. The likelihood integrals are evaluated by a Gaussian quadrature and Gaussian quadrature particle filter. The bootstrap or sequential importance resampling particle filter is used as an accuracy benchmark. The posterior is estimated by the Gaussian filter and a Metropolis Hastings algorithm. I propose a genetic extension of the standard MetropolisHastings algorithm by parallel random walk sequences. This improves the robustness of start values and the global maximization properties. Moreover it simplifies a cluster implementation and the random walk variances decision is reduced to only two parameters so that almost no trial sequences are needed. Finally the marginal likelihood is calculated as a criterion for nonnested and quasitrue models in order to select between the nonlinear estimates and a first order perturbation solution combined with the Kalman filter. ; stochastic dynamic general equilibrium model, Chebyshev polynomials, Smolyak operator, nonlinear state space filter, Curse of Dimensionality, posterior of structural parameters, marginal likelihood
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Document Type:
preprint
URL:
http://129.3.20.41/eps/ge/papers/0507/0507014.pdf
http://129.3.20.41/eps/ge/papers/0507/0507014.pdf
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RePEc: Research Papers in Economics
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2.
JBendge: An ObjectOriented System for Solving, Estimating and Selecting Nonlinear Dynamic Models
Title:
JBendge: An ObjectOriented System for Solving, Estimating and Selecting Nonlinear Dynamic Models
Author:
Viktor Winschel
;
Markus Krätzig
Viktor Winschel
;
Markus Krätzig
Minimize authors
Description:
We present an objectoriented software framework allowing to specify, solve, and estimate nonlinear dynamic general equilibrium (DSGE) models. The imple mented solution methods for nding the unknown policy function are the standard linearization around the deterministic steady state, and a function iterator using a multivariate global Chebyshev...
We present an objectoriented software framework allowing to specify, solve, and estimate nonlinear dynamic general equilibrium (DSGE) models. The imple mented solution methods for nding 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 lters. The estimation step is done by a parallel MetropolisHastings (MH) algorithm, using a linear or nonlinear lter. Implemented are the Kalman, Extended Kalman, Particle, Smolyak Kalman, Smolyak Sum, and Smolyak Kalman Particle lters. 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 benet from multiprocessor environments. JBendge is based on the framework JStatCom, which provides a standardized ap plication interface. All tasks are supported by an elaborate multithreaded graphical user interface (GUI) with project management and data handling facilities. ; Dynamic Stochastic General Equilibrium (DSGE) Models, Bayesian Time Series Econometrics, Java, Software Development
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Document Type:
preprint
URL:
http://sfb649.wiwi.huberlin.de/papers/pdf/SFB649DP2008034.pdf
http://sfb649.wiwi.huberlin.de/papers/pdf/SFB649DP2008034.pdf
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RePEc: Research Papers in Economics
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3.
Solving, Estimating and Selecting Nonlinear Dynamic Models without the Curse of Dimensionality
Title:
Solving, Estimating and Selecting Nonlinear Dynamic Models without the Curse of Dimensionality
Author:
Viktor Winschel
;
Markus Krätzig
Viktor Winschel
;
Markus Krätzig
Minimize authors
Description:
We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. TheSmolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the model solution. The operator also eliminates the cur...
We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. TheSmolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the model solution. The operator also eliminates the curse from Gaussian quadrature and we use it for the integrals arising from rational expectations and in three new nonlinear state space filters. The filters substantially decrease the computational burden compared to the sequential importance resampling particle filter. The posterior of the structural parameters is estimated by a new MetropolisHastings algorithm with mixing parallel sequences. The parallel extension improves the global maximization property of the algorithm, simplifies the choice of the innovation variances, allows for unbiased convergence diagnostics and for a simple implementation of the estimation on parallel computers. Finally, we provide all algorithms in the open source software JBendge4 for the solution and estimation of a general class of models. ; Dynamic Stochastic General Equilibrium (DSGE) Models, Bayesian Time Series Econometrics, Curse of Dimensionality
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Document Type:
preprint
URL:
http://sfb649.wiwi.huberlin.de/papers/pdf/SFB649DP2008018.pdf
http://sfb649.wiwi.huberlin.de/papers/pdf/SFB649DP2008018.pdf
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4.
Solving, Estimating, and Selecting Nonlinear Dynamic Models Without the Curse of Dimensionality
Title:
Solving, Estimating, and Selecting Nonlinear Dynamic Models Without the Curse of Dimensionality
Author:
Viktor Winschel
;
Markus Kr‰tzig
Viktor Winschel
;
Markus Kr‰tzig
Minimize authors
Description:
We 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 ne...
We 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 MetropolisHastings 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.
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Document Type:
article
URL:
http://hdl.handle.net/10.3982/ECTA6297
http://hdl.handle.net/10.3982/ECTA6297
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5.
Solving, Estimating and Selecting Nonlinear Dynamic Models 1
Open Access
Title:
Solving, Estimating and Selecting Nonlinear Dynamic Models 1
Author:
Jbendge An Object
;
Viktor Winschel
;
Viktor Winschel
;
Markus Krätzig A
Jbendge An Object
;
Viktor Winschel
;
Viktor Winschel
;
Markus Krätzig A
Minimize authors
Description:
We present an objectoriented software framework allowing to specify, solve, and estimate nonlinear dynamic general equilibrium (DSGE) models. The implemented 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...
We present an objectoriented software framework allowing to specify, solve, and estimate nonlinear dynamic general equilibrium (DSGE) models. The implemented 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 operator to overcome the course of dimensionality. The operator is also useful for numerical integration and we use it for the integrals arising in rational expectations and in nonlinear state space filters. The estimation step is done by a parallel MetropolisHastings (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 monitored and controlled interactively 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 application interface. All tasks are supported by an elaborate multithreaded graphical user interface (GUI) with project management and data handling facilities.
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20100306
Source:
http://edoc.huberlin.de/series/sfb649papers/200834/PDF/34.pdf
http://edoc.huberlin.de/series/sfb649papers/200834/PDF/34.pdf
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Document Type:
text
Language:
en
Subjects:
Dynamic Stochastic General Equilibrium (DSGE) Models ; Bayesian Time Series Econometrics ; Java ; Software Development
Dynamic Stochastic General Equilibrium (DSGE) Models ; Bayesian Time Series Econometrics ; Java ; Software Development
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DDC:
518 Numerical analysis
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.2104
http://edoc.huberlin.de/series/sfb649papers/200834/PDF/34.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.2104
http://edoc.huberlin.de/series/sfb649papers/200834/PDF/34.pdf
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6.
Solving, Estimating and Selecting Nonlinear Dynamic Models without the Curse of Dimensionality
Open Access
Title:
Solving, Estimating and Selecting Nonlinear Dynamic Models without the Curse of Dimensionality
Author:
Viktor Winschel
;
Markus Krätzig
Viktor Winschel
;
Markus Krätzig
Minimize authors
Description:
We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. The Smolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the model solution. The operator also eliminates the cu...
We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. The Smolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the model solution. The operator also eliminates the curse from Gaussian quadrature and we use it for the integrals arising from rational expectations and in three new nonlinear state space filters. The filters substantially decrease the computational burden compared to the sequential importance resampling particle filter. The posterior of the structural parameters is estimated by a new MetropolisHastings algorithm with mixing parallel sequences. The parallel extension improves the global maximization property of the algorithm, simplifies the choice of the innovation variances, allows for unbiased convergence diagnostics and for a simple implementation of the estimation on parallel computers. Finally, we provide all algorithms in the open source software JBendge4 for the solution and estimation of a general class of models.
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20100408
Source:
http://edoc.huberlin.de/series/sfb649papers/200818/PDF/18.pdf
http://edoc.huberlin.de/series/sfb649papers/200818/PDF/18.pdf
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Document Type:
text
Language:
en
Subjects:
Dynamic Stochastic General Equilibrium (DSGE) Models ; Bayesian Time Series Econometrics ; Curse of Dimensionality
Dynamic Stochastic General Equilibrium (DSGE) Models ; Bayesian Time Series Econometrics ; Curse of Dimensionality
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DDC:
518 Numerical analysis
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.1996
http://edoc.huberlin.de/series/sfb649papers/200818/PDF/18.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.1996
http://edoc.huberlin.de/series/sfb649papers/200818/PDF/18.pdf
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7.
Bayes Network Analysis of Trade Effects of Currency Unions and Free Trade Agreements
Open Access
Title:
Bayes Network Analysis of Trade Effects of Currency Unions and Free Trade Agreements
Author:
Phillip Baur
;
Viktor Winschel
Phillip Baur
;
Viktor Winschel
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Description:
We investigate the effects of trade and currency unions on trade within the reduced form gravity model. The database is a standard database in this field for around 200 countries over the last fifty years. The statistical model we use is a Bayes network from the machine learning and the artificial intelligence research which generalizes many sta...
We investigate the effects of trade and currency unions on trade within the reduced form gravity model. The database is a standard database in this field for around 200 countries over the last fifty years. The statistical model we use is a Bayes network from the machine learning and the artificial intelligence research which generalizes many standard econometric models. The emphasis in this paper is on a simplified specification of models with discrete and continuous variables and on simultaneous equations time series models. The results indicate that trade and currency unions have substantially lower effects on trade than derived in previous empirical work which we reproduce as special cases of our statistical framework. 1 1
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20100224
Source:
http://www.vwl.unimannheim.de/
winschel
/docs/TradeBayesNetworks.pdf
http://www.vwl.unimannheim.de/
winschel
/docs/TradeBayesNetworks.pdf
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Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.153.6774
http://www.vwl.unimannheim.de/winschel/docs/TradeBayesNetworks.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.153.6774
http://www.vwl.unimannheim.de/winschel/docs/TradeBayesNetworks.pdf
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8.
Categorical Methods at the Crossroads (Dagstuhl Perspectives Workshop 14182)
Open Access
Title:
Categorical Methods at the Crossroads (Dagstuhl Perspectives Workshop 14182)
Author:
Abramsky, Samson
;
Baez, John C.
;
Gadducci, Fabio
;
Winschel, Viktor
Abramsky, Samson
;
Baez, John C.
;
Gadducci, Fabio
;
Winschel, Viktor
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Description:
This report documents the program and the outcomes of Dagstuhl Seminar 14182 "Perspectives Workshop: Categorical Methods at the Crossroads". The aim of the meeting was to investigate the potential of category theory as a paradigm for mathematical modeling and applied science. The envisaged application areas included computation, physics, biology...
This report documents the program and the outcomes of Dagstuhl Seminar 14182 "Perspectives Workshop: Categorical Methods at the Crossroads". The aim of the meeting was to investigate the potential of category theory as a paradigm for mathematical modeling and applied science. The envisaged application areas included computation, physics, biology, complex systems, social and cognitive science and linguistics. Many of these areas were indeed tackled in the variety of topics dealt with during the workshop. Each working day followed the same structure: two survey lectures during the morning, followed by two/three shorter talks in the afternoon, and closed by a working group session. During these sessions the attendants split into several groups according to the main thematic areas that had been identified on the first day. Both surveys and talks are reported in the "Overview" section of the report, while a wrapup of the discussions that occurred inside the working groups is reported in the "Working Groups" section.
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Publisher:
Schloss Dagstuhl  LeibnizZentrum fuer Informatik
Contributors:
Samson Abramsky and John C. Baez and Fabio Gadducci and Viktor Winschel
Year of Publication:
2014
Source:
Dagstuhl Reports, Volume 4, Issue 4; ISSN 21925283
Dagstuhl Reports, Volume 4, Issue 4; ISSN 21925283
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Document Type:
Report / Research Paper / Working Paper ; InProceedings
Language:
English
Subjects:
Category theory ; concurrency ; economics ; game theory ; logics ; quantum computing ; semantics
Category theory ; concurrency ; economics ; game theory ; logics ; quantum computing ; semantics
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Rights:
http://creativecommons.org/licenses/
http://creativecommons.org/licenses/
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URL:
http://nbnresolving.de/urn:nbn:de:0030drops46189
http://drops.dagstuhl.de/opus/volltexte/2014/4618/
http://nbnresolving.de/urn:nbn:de:0030drops46189
http://drops.dagstuhl.de/opus/volltexte/2014/4618/
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9.
JBendge ; An ObjectOriented System for Solving, Estimating and Selecting Nonlinear Dynamic Models
Open Access
Title:
JBendge ; An ObjectOriented System for Solving, Estimating and Selecting Nonlinear Dynamic Models
Author:
Winschel, Viktor
;
Krätzig, Markus
Winschel, Viktor
;
Krätzig, Markus
Minimize authors
Description:
We present an objectoriented 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 Chebysh...
We present an objectoriented 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 MetropolisHastings (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 multithreaded graphical user interface (GUI) with project management and data handling facilities.
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Publisher:
Humboldt University Berlin, Germany
Year of Publication:
20080425
Document Type:
Text ; doctype:PeriodicalPart ; doctype:Reports
Language:
eng
Subjects:
Wirtschaft ; Dynamic Stochastic General Equilibrium (DSGE) Models ; Bayesian Time Series Econometrics ; Java ; Software Development ; ddc:330
Wirtschaft ; Dynamic Stochastic General Equilibrium (DSGE) Models ; Bayesian Time Series Econometrics ; Java ; Software Development ; ddc:330
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DDC:
330 Economics
;
518 Numerical analysis
(computed)
URL:
http://edoc.huberlin.de/docviews/abstract.php?id=28958
http://edoc.huberlin.de/series/sfb649papers/200834/PDF/34.pdf
http://edoc.huberlin.de/docviews/abstract.php?id=28958
http://edoc.huberlin.de/series/sfb649papers/200834/PDF/34.pdf
http://www.nbnresolving.de/urn:nbn:de:kobv:1110088581
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10.
Solving, Estimating and Selecting Nonlinear Dynamic Models without the Curse of Dimensionality
Open Access
Title:
Solving, Estimating and Selecting Nonlinear Dynamic Models without the Curse of Dimensionality
Author:
Winschel, Viktor
;
Krätzig, Markus
Winschel, Viktor
;
Krätzig, Markus
Minimize authors
Description:
We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. TheSmolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the model solution. The operator also eliminates the cur...
We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. TheSmolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the model solution. The operator also eliminates the curse from Gaussian quadrature and we use it for the integrals arising from rational expectations and in three new nonlinear state space filters. The filters substantially decrease the computational burden compared to the sequential importance resampling particle filter. The posterior of the structural parameters is estimated by a new MetropolisHastings algorithm with mixing parallel sequences. The parallel extension improves the global maximization property of the algorithm, simplifies the choice of the innovation variances, allows for unbiased convergence diagnostics and for a simple implementation of the estimation on parallel computers. Finally, we provide all algorithms in the open source software JBendge4 for the solution and estimation of a general class of models.
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Publisher:
Humboldt University Berlin, Germany
Year of Publication:
20080207
Document Type:
Text ; doctype:PeriodicalPart ; doctype:Reports
Language:
eng
Subjects:
Wirtschaft ; Dynamic Stochastic General Equilibrium (DSGE) Models ; Bayesian Time Series Econometrics ; Curse of Dimensionality ; ddc:330
Wirtschaft ; Dynamic Stochastic General Equilibrium (DSGE) Models ; Bayesian Time Series Econometrics ; Curse of Dimensionality ; ddc:330
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DDC:
330 Economics
;
518 Numerical analysis
(computed)
URL:
http://edoc.huberlin.de/docviews/abstract.php?id=28615
http://edoc.huberlin.de/series/sfb649papers/200818/PDF/18.pdf
http://edoc.huberlin.de/docviews/abstract.php?id=28615
http://edoc.huberlin.de/series/sfb649papers/200818/PDF/18.pdf
http://www.nbnresolving.de/urn:nbn:de:kobv:1110085173
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(3) software development
(3) theorie
(2) allgemeines gleichgewicht
(2) borrowing costs
(2) c11
(2) c13
(2) c32
(2) c52
(2) c63
(2) c68
(2) c87
(2) computer science computer science and game theory
(2) economics ddc 300
(2) emu
(2) government deficits
(2) market discipline
(2) public debt
(2) wirtschaft
(1) 000 computer science
(1) baye sian time series econometrics
(1) bayes statistik
(1) c25
(1) category theory
(1) cge modelling
(1) computer science
(1) computer science logic in computer science
(1) concurrency
(1) ddc 300
(1) diskussionsbeiträge
(1) dynamisches gleichgewicht
(1) economics
(1) estimation
(1) game theory
(1) general works
(1) h62
(1) jel h62
(1) kapitalkosten
(1) kapitalkosten stw
(1) knowledge
(1) logics
(1) mathematics logic
(1) messung
(1) messung stw
(1) mixed logit
(1) nichtlineare dynamische systeme
(1) objektorientierte programmierung
(1) oecd staaten
(1) oecd staaten stw
(1) pc software
(1) pc software stw
(1) public choice
(1) public choice stw
(1) quadrature
(1) quantum computing
(1) realzins
(1) realzins stw
(1) rendite
(1) rendite stw
(1) risiko
(1) risiko stw
(1) semantics
(1) simulation
(1) statistische methoden
(1) stochastischer prozess
(1) theorie stw
(1) value at risk
(1) value at risk stw
(1) volkswirtschaft
(1) zeitreihenanalyse
(1) öffentliche schulden
(1) öffentliche schulden stw
(1) öffentlicher haushalt
(1) öffentlicher haushalt stw
(1) ökonometrisches modell
Subject:
Dewey Decimal Classification (DDC)
(8) Economics [33*]
(8) Mathematics [51*]
(2) Social sciences, sociology & anthropology...
(1) Computer science, knowledge & systems [00*]
(1) Science [50*]
Dewey Decimal Classification (DDC):
Year of Publication
(5) 2014
(4) 2008
(3) 2001
(3) 2010
(2) 1996
(2) 2006
(2) 2012
Year of Publication:
Content Provider
(8) RePEc.org
(5) EconStor
(4) Mannheim Univ.: MADOC
(3) CiteSeerX
(3) LeibnizOpen
(2) ArXiv.org
(2) Berlin HU: edoc
(1) DataCite Metadata Store
(1) Munich LMU: Open Access
Content Provider:
Language
(14) Unknown
(13) English
(2) German
Language:
Document Type
(12) Reports, Papers, Lectures
(9) Article, Journals
(8) Text
Document Type:
Access
(15) Open Access
(14) Unknown
Access:
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