  Macro-economic models describe the dynamics of economic quantities. The
estimations and forecasts produced by such models play a substantial role for
financial and political decisions. In this contribution we describe an approach
based on genetic programming and symbolic regression to identify variable
interactions in large datasets. In the proposed approach multiple symbolic
regression runs are executed for each variable of the dataset to find
potentially interesting models. The result is a variable interaction network
that describes which variables are most relevant for the approximation of each
variable of the dataset. This approach is applied to a macro-economic dataset
with monthly observations of important economic indicators in order to identify
potentially interesting dependencies of these indicators. The resulting
interaction network of macro-economic indicators is briefly discussed and two
of the identified models are presented in detail. The two models approximate
the help wanted index and the CPI inflation in the US.
