Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/237993
Year of Publication: 
2020
Series/Report no.: 
BGPE Discussion Paper No. 202
Publisher: 
Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen und Nürnberg
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, introduce to the theory behind PCE, analyze the convergence behavior for different 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 efficiency.
Subjects: 
Polynomial Chaos Expansion
parameter inference
parameter uncertainty
solution methods
JEL: 
C11
C13
C32
C63
Document Type: 
Working Paper

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