Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195551 
Year of Publication: 
2017
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 8 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2017 [Pages:] 651-683
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
Approximating stochastic processes by finite-state Markov chains is useful for reducing computational complexity when solving dynamic economic models. We provide a new method for accurately discretizing general Markov processes by matching low order moments of the conditional distributions using maximum entropy. In contrast to existing methods, our approach is not limited to linear Gaussian autoregressive processes. We apply our method to numerically solve asset pricing models with various underlying stochastic processes for the fundamentals, including a rare disasters model. Our method outperforms the solution accuracy of existing methods by orders of magnitude, while drastically simplifying the solution algorithm. The performance of our method is robust to parameters such as the number of grid points and the persistence of the process.
Subjects: 
Asset pricing models
duality
Kullback-Leibler information
numerical methods
solution accuracy
JEL: 
C63
C68
G12
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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