Polynomial chaos expansion: Efficient evaluation and estimation of computational models - Daniel Fehrle; Christopher Heiberger; Johannes Huber (Universität Augsburg, Institut für Volkswirtschaftslehre, 2020)
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.
urn:nbn:de:bvb:384-opus4-1026317
Keywords: C11; C13; C32; C63; 330; Polynomial Chaos Expansion; parameter inference; parameter uncertainty; solution methods