Fat-tail Distributions and Business-Cycle Models

By Guido Ascari, Giorgio Fagiolo and Andrea Roventini


Recent empirical findings suggest that macroeconomic variables are seldom normally distributed. For example, the distributions of aggregate output growth-rate time series of many OECD countries are well approximated by symmetric exponential-power (EP) densities, with Laplace fat tails. In this work, we assess whether Real Business Cycle (RBC) and standard medium-scale New-Keynesian (NK) models are able to replicate this statistical regularity. We simulate both models drawing Gaussian- vs Laplace-distributed shocks and we explore the statistical properties of simulated time series. Our results cast doubts on whether RBC and NK models are able to provide a satisfactory representation of the transmission mechanisms linking exogenous shocks to macroeconomic dynamics.

Much of economic (and econometric) analysis is based on the assumption that stochstic processes are normal. This paper claims that at least some aggregate statistics are not normal — in particular they have fat tails–, and that the RBC and NK models cannot replicate that. I am not sure this is a valid criticism of the models. That is, however, a valid criticism of the solution method of those models. It is convenient to use linear-quadratic methods to solve them, and they require normal distributions, but it does noat have to be those.

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