Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/273635 
Year of Publication: 
2022
Series/Report no.: 
LEM Working Paper Series No. 2022/33
Publisher: 
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), Pisa
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 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.
Subjects: 
Calibration
Validation
Simulation models
SVAR models
Causal inference
Model confidence sets
Independent component analysis
JEL: 
C32
C52
E37
Document Type: 
Working Paper

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