Calibration and validation of macroeconomic simulation models by statistical causal search
提出了一种宏观经济模型校准与验证的通用方法，通过统计因果搜索识别参数，并应用于气候变化与经济增长关系的复杂模拟模型。
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.