Polynomial chaos expansion: Efficient evaluation and estimation of computational models

Daniel Fehrle, Christopher Heiberger () and Johannes Huber
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Christopher Heiberger: University of Augsburg, Department of Economics

No 341, Discussion Paper Series from Universitaet Augsburg, Institute for Economics

Abstract: Polynomial chaos expansion (PCE) provides a method that enables the user to representa quantity of interest (QoI) of a model's solution as a series expansion of uncertainmodel inputs, usually its parameters. Among the QoIs are the policy function,the second moments of observables, or the posterior kernel. Hence, PCE sidesteps therepeated and time consuming evaluations of the model's outcomes.The paper discusses the suitability of PCE for computational economics. We, therefore,introduce to the theory behind PCE, analyze the convergence behavior for differentelements of the solution of the standard real business cycle model as illustrativeexample, and check the accuracy, if standard empirical methods are applied. Theresults are promising, both in terms of accuracy and efficiency.

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)
Date: 2020-12
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ore
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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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