Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/284194 
Autor:innen: 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
School of Economics Discussion Papers No. 2203
Verlag: 
University of Kent, School of Economics, Canterbury
Zusammenfassung: 
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.
Schlagwörter: 
Bayesian estimation
surrogate methods
Gaussian process
simulation models
JEL: 
C14
C15
C52
C63
Dokumentart: 
Working Paper

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