Abstract. 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.
In REF , multiple genetic programming based symbolic regression runs are executed for each variable separately and a variable interaction network is built by identifying the most relevant variables for a given target variable in terms of a In this section, we present three approaches to automatically extract the hierarchical relationships in multivariate data.
Macro-Economic Time Series Modeling and Interaction Networks
Applications of Evolutionary Computation, LNCS 6625 (Springer Berlin Heidelberg), pp. 101-110 (2011)
Computer Science Mathematics