Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323340 
Year of Publication: 
2025
Citation: 
[Journal:] Computational Economics [ISSN:] 1572-9974 [Volume:] 65 [Issue:] 2 [Publisher:] Springer US [Place:] New York, NY [Year:] 2025 [Pages:] 1083-1146
Publisher: 
Springer US, New York, NY
Abstract: 
We apply Polynomial chaos expansion (PCE) to surrogate time-consuming repeated model evaluations for different parameter values. PCE represents a random variable, the quantity of interest (QoI), as a series expansion of other random variables, the inputs. Repeated evaluations become inexpensive by treating uncertain parameters of a model as inputs, and an element of a model’s solution, e.g., the policy function, second moments, or the posterior kernel as the QoI. We introduce the theory of PCE and apply it to the standard real business cycle model as an illustrative example. We analyze the convergence behavior of PCE for different QoIs and its efficiency when used for estimation. The results are promising both for local and global solution methods.
Subjects: 
Polynomial chaos expansion
Parameter inference
Parameter uncertainty
Solution methods
JEL: 
C11
C13
C32
C63
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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