Polynomial chaos expansion: Efficient evaluation and estimation of computational models

Daniel Fehrle, Christopher Heiberger and Johannes Huber

No 202, Working Papers from Bavarian Graduate Program in Economics (BGPE)

Abstract: Polynomial chaos expansion (PCE) provides a method that enables the user to represent a quantity of interest (QoI) of a model’s solution as a series expansion of uncertain model inputs, usually its parameters. Among the QoIs are the policy function, the second moments of observables, or the posterior kernel. Hence, PCE sidesteps the repeated and time consuming evaluations of the model’s outcomes. The paper discusses the suitability of PCE for computational economics. We, therefore, introducetothetheorybehindPCE, analyzetheconvergencebehaviorfordifferent elements of the solution of the standard real business cycle model as illustrative example, and check the accuracy, if standard empirical methods are applied. The results are promising, both in terms of accuracy and efï¬ ciency.

Keywords: Polynomial Chaos Expansion; parameter inference; parameter uncertainty; solution methods (search for similar items in EconPapers)
JEL-codes: C11 C13 C32 C63 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2020-12
New Economics Papers: this item is included in nep-cmp and nep-ore
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https://bgpe.cms.rrze.uni-erlangen.de/files/2023/0 ... utational-models.pdf First version, 2020 (application/pdf)

Related works:
Journal Article: Polynomial Chaos Expansion: Efficient Evaluation and Estimation of Computational Models (2025)
Working Paper: Polynomial chaos expansion: Efficient evaluation and estimation of computational models (2020)
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