Network Calibration and Metamodeling of a Financial Accelerator Agent Based Model

Leonardo Bargigli (), Luca Riccetti, Alberto Russo and Mauro Gallegati

Working Papers - Economics from Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa

Abstract: We allow firms and banks to entertain multiple credit connections in a financially constrained production framework, resorting to a random network model whose parameters are calibrated with real data. The calibration is successful since the network model is able to reproduce the degree and strength (debt and loan) distributions of the Japanese credit market. We run simulations over the parameter space using an efficient design, and compare a number of alternative statistical metamodels in order to select the best specification for the relationship between the parameters and a set of endogenous variables of the model. We show that the metamodeling approach can be usefully extended to economic models in order to bridge the gap between micro and macro variables through a rigorous statistical analysis of ABMs, without imposing unrealistic restrictions on the micro model such as the representative agent hypothesis.

Pages: 36 pages
Date: 2016
New Economics Papers: this item is included in nep-cmp and nep-hme
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Citations: View citations in EconPapers (28)

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Related works:
Journal Article: Network calibration and metamodeling of a financial accelerator agent based model (2020)
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