Micro Heterogeneity and Macro Dynamics: an Empirical Analysis

Recent developments in the aggregation of large cross section of linear time series processes provide a complete characterization of the link between the dynamic properties of the aggregate series and the shape and degree of heterogeneity of the coefficients of the micro time series (see Lippi and Zaffaroni, 1999). These statistical results emphasizes the relevance of the heterogeneity in understanding the properties of the aggregates and that even mild difference in the form of heterogeneity can imply relevant difference at macro level. Those results allow to close the gap between micro and macro behavior, at least in the case when the reduced form dynamics at micro level derived from general equilibrium models can be well approximated by an ARMA with exogenous regressors, which represents the exact solution of linear-quadratic dynamic programming models of intertemporal optimization. We use the PSID data set. Although the micro processes appear to be stationary, the form and degree of micro parameters heterogenity accounts for the nonstationarity and persistence observed in aggregate data, well described by the long memory paradigm. Further, we find evidence of both a common and an idiosyncratic component, at the micro level which, in contrast to a common belief, appear to be equally important for aggregate dynamics.