Artikel
Discretizing nonlinear, non-Gaussian Markov processes with exact conditional moments
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 8 ; Year: 2017 ; Issue: 2 ; Pages: 651-683 ; New Haven, CT: The Econometric Society
- Klassifikation
-
Wirtschaft
Computational Techniques; Simulation Modeling
Computable General Equilibrium Models
Asset Pricing; Trading Volume; Bond Interest Rates
- Thema
-
Asset pricing models
duality
Kullback-Leibler information
numerical methods
solution accuracy
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Farmer, Leland E.
Toda, Alexis Akira
- Ereignis
-
Veröffentlichung
- (wer)
-
The Econometric Society
- (wo)
-
New Haven, CT
- (wann)
-
2017
- DOI
-
doi:10.3982/QE737
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Farmer, Leland E.
- Toda, Alexis Akira
- The Econometric Society
Entstanden
- 2017
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