Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195535 
Year of Publication: 
2017
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 8 [Issue:] 1 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2017 [Pages:] 117-147
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
This paper introduces a nonlinear certainty-equivalent approximation method for dynamic stochastic problems. We first introduce a novel, stable, and efficient method for computing the decision rules in deterministic dynamic economic problems. We use the results as nonlinear and global certainty-equivalent approx- imations for solutions to stochastic problems, and compare their accuracy to the common linear and local certainty-equivalent methods. Our examples demon- strate that this method can be applied to solve high-dimensional problems with up to 400 state variables with acceptable accuracy. This method can also be applied to solve problems with inequality constraints. These features make the non- linear certainty-equivalent approximation method suitable for solving complex economic problems, where other algorithms, such as log-linearization, fail to produce a valid global approximation or are far less tractable.
Subjects: 
New Keynesian DSGE model
competitive equilibrium
parallel computing
sparse grid approximation
real business cycle model
JEL: 
C61
C63
C68
E31
E52
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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