Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In recent years, a growing stream of literature has investigated the credit market from a network perspective, highlighting the systemic effects of sectoral or idiosyncratic shocks. Models within this literature have to contain the number of possible agents and interaction channels in order for the models to be tractable, or, in case of large-scale ones such as agent-based models, the only possible solution is numerical. This paper proposes a novel approach to the representation of networks in macroeconomics, and presents a credit network model that is solved using statistical physics methods. This approach extends and enriches the network literature by providing an analytical representation of the dynamic evolution of the network structure during the cycle.