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Title:Solving, estimating and selecting nonlinear dynamic models without the curse of dimensionality PDF Logo
Authors:Winschel, Viktor
Krätzig, Markus
Issue Date:2008
Series/Report no.:SFB 649 discussion paper 2008,018
Abstract: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 Metropolis-Hastings 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 JBendge for the solution and estimation of a general class of models.
Subjects:Dynamic Stochastic General Equilibrium (DSGE) Models
Baye- sian Time Series Econometrics
Curse of Dimensionality
JEL:C11
C13
C15
C32
C52
C63
C68
C87
Document Type:Working Paper
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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