Template-Type: ReDIF-Paper 1.0

Author-Name: Daniel Fehrle
Author-X-Name-First: Daniel
Author-X-Name-Last: Fehrle

Author-Name: Christopher Heiberger
Author-X-Name-First:Christopher  
Author-X-Name-Last: Heiberger

Author-Name: Johannes Huber
Author-X-Name-First: Johannes 
Author-X-Name-Last: Huber

Title: Polynomial chaos expansion: Efficient evaluation and estimation of computational models
	
	
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 efficiency.

Length: 49 pages
Creation-Date:  2020-12
File-URL: http://www.bgpe.de/texte/DP/202_Fehrle_Heiberger_Huber.pdf
File-Format: Application/pdf
File-Function: First version, 2020
Number: 202
Classification-JEL: C11,C13,C32,C63

Keywords:  Polynomial Chaos Expansion, parameter inference, parameter uncertainty, solution methods


Handle: RePEc:bav:wpaper:202_FehrleHeibergerHuber