Calibration and validation of macroeconomic simulation models by statistical causal search

We introduce a general procedure for macroeconomic models’ calibration and validation. Configurations of parameters are selected on the basis of a loss function involving a distance between model-derived structural coefficients and their empirical counterparts. These, in both cases, are locally identified by exploiting non-Gaussianity in a structural vector autoregressive framework under a data-driven approach. We use model confidence set to account for the uncertainty in the selection procedure. We provide a measure of validation by comparing (model’s and empirical) shocks-variables structure. We apply our procedure to a complex macroeconomic simulation model that studies the link between climate change and economic growth.