2022/33 | LEM Working Paper Series | ||||||||||||||||
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Calibration and Validation of Macroeconomic Simulation Models by Statistical Causal Search |
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Mario Martinoli, Alessio Moneta and Gianluca Pallante |
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Keywords | |||||||||||||||||
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Calibration; Validation; Simulation models; SVAR models; Causal inference;
Model confidence sets; Independent component analysis.
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JEL Classifications | |||||||||||||||||
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C32, C52, E37
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Abstract | |||||||||||||||||
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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 re-
strictions. 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.
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