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Bayesian Estimation of Large-Scale Simulation Models with Gaussian Process Regression Surrogates

Sylvain Barde

Studies in Economics from School of Economics, University of Kent

Abstract: Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their parameters from empirical data. Most simulation models do not possess closed form expressions for their likelihood function, requiring the use of simulation-based inference, such as simulated method of moments, indirect inference or approximate Bayesian computation. However, given the high computational requirements of large-scale models, it is often difficult to run these estimation methods, as they require more simulated runs that can feasibly be carried out. This paper aims to address the problem by providing a full Bayesian estimation framework where the true but intractable likelihood function of the simulation model is replaced by one generated by a surrogate model. This is provided by a sparse variational Gaussian process, chosen for its desirable convergence and consistency properties. The effectiveness of the approach is tested using both a Monte Carlo analysis on a known data generating process, and an empirical application in which the free parameters of a computationally demanding agent-based model are estimated on US macroeconomic data.

Keywords: Bayesian estimation; surrogate methods; Gaussian process; simulation models (search for similar items in EconPapers)
JEL-codes: C14 C15 C52 C63 (search for similar items in EconPapers)
Date: 2022-08
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Citations: View citations in EconPapers (1)

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