Calibration and Validation of Macroeconomic Simulation Models by Statistical Causal Search

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.