Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates

Sylvain Barde

Computational Statistics & Data Analysis, 2024, vol. 196, issue C

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, likelihood-free 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. The aim is 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 trained on the limited simulated data. This is provided by a Linear Model of Coregionalization, where each latent variable is a sparse variational Gaussian process, chosen for its desirable convergence and consistency properties. The effectiveness of the approach is tested using both a simulated Bayesian computing 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: Linear model of coregionalization; Likelihood-free inference; Variational inference; Agent-based model; Non-parametric method (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Working Paper: Bayesian Estimation of Large-Scale Simulation Models with Gaussian Process Regression Surrogates (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:196:y:2024:i:c:s0167947324000562

DOI: 10.1016/j.csda.2024.107972

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