Network calibration and metamodeling of a financial accelerator agent based model

Leonardo Bargigli, Luca Riccetti, Alberto Russo and Mauro Gallegati

Journal of Economic Interaction and Coordination, 2020, vol. 15, issue 2, No 4, 413-440

Abstract: Abstract 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 $$h(\theta ,s)$$h(θ,s) through the following steps: (1) we run agent-based simulations using an efficient sampling design of the parameter space $$\Theta $$Θ; (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 $$\theta \in \Theta $$θ∈Θ. 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?”.

Date: 2020
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Working Paper: Network Calibration and Metamodeling of a Financial Accelerator Agent Based Model (2016)
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DOI: 10.1007/s11403-018-0217-8

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