Calibration and Validation of Macroeconomic Simulation Models by Statistical Causal Search

Mario Martinoli, Alessio Moneta and Gianluca Pallante

LEM Papers Series from Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy

Abstract: We propose a general protocol for calibration and validation of complex simulation models by an approach based on discovery and comparison of causal structures. The key idea is that configurations of parameters of a given theoretical model are selected by minimizing a distance index between two structural models: one estimated from the data generated by the theoretical model, another estimated from a set of observed data. Validation is conceived as a measure of matching between the theoretical and the empirical causal structure. Causal structures are identified combining structural vector autoregressive and independent component analysis, so as to avoid a priori restrictions. We use model confidence set as a tool to measure the uncertainty associated to the alternative configurations of parameters and causal structures. We illustrate the procedure by applying it to a large-scale macroeconomic agent-based model, namely the ''dystopian Schumpeter-meeting-Keynes'' model.

Keywords: Calibration; Validation; Simulation models; SVAR models; Causal inference; Model confidence sets; Independent component analysis. (search for similar items in EconPapers)
Date: 2022-10-24
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-hme
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ssa:lemwps:2022/33

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