Network calibration and metamodeling of a financial accelerator agent based model

We introduce a simple financially constrained production framework in which heterogeneous firms and banks maintain multiple credit connections. The parameters of credit market interaction are estimated from real data in order to reproduce a set of empirical regularities of the Japanese credit market. We then pursue the metamodeling approach, i.e. we derive a reduced form for a set of simulated moments $$h(\theta ,s)$$ through the following steps: (1) we run agent-based simulations using an efficient sampling design of the parameter space $$\Theta $$ ; (2) we employ the simulated data to estimate and then compare a number of alternative statistical metamodels. Then, using the best fitting metamodels, we study through sensitivity analysis the effects on h of variations in the components of $$\theta \in \Theta $$ . Finally, we employ the same approach to calibrate our agent-based model (ABM) with Japanese data. Notwithstanding the fact that our simple model is rejected by the evidence, we show th at metamodels can provide a methodologically robust answer to the question “does the ABM replicate empirical data?”.