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Solomon and Golo [1] have recently proposed an autocatalytic(self-reinforcing) feedback model which couples a macroscopic system parameter(the interest rate), a microscopic parameter that measures the distribution ofthe states of the individual agents (the number of firms in financialdifficulty) and a peer-to-peer network effect (contagion across supply chainfinancing). In this model, each financial agent is characterized by itsresilience to the interest rate. Above a certain rate the interest due on thefirm's financial costs exceeds its earnings and the firm becomes susceptible tofailure (ponzi). For the interest rate levels under a certain threshold level,the firm loans are smaller then its earnings and the firm becomes 'hedge.' Inthis paper, we fit the historical data (2002-2009) on interest rate data intoour model, in order to predict the number of the ponzi firms. We compare theprediction with the data taken from a large panel of Italian firms over aperiod of 9 years. We then use trade credit linkages to discuss the connectionbetween the ponzi density and the network percolation.  We find that the 'top-down'-'bottom-up' positive feedback loop accounts formost of the Minsky crisis accelerator dynamics. The peer-to-peer ponzicompanies contagion becomes significant only in the last stage of the crisiswhen the ponzi density is above a critical value. Moreover the ponzi contagionis limited only to the companies that were not dynamic enough to substitutetheir distressed clients with new ones. In this respect the data support a viewin which the success of the economy depends on substituting the static'supply-network' picture with an interacting dynamic agents one.

Too dynamic to fail. Empirical support for an autocatalytic model of Minsky's financial instability hypothesis