Macro-Economic Time Series Modeling and Interaction Networks

TL;DR

This paper introduces a novel method using genetic programming and symbolic regression to uncover variable interactions in macro-economic datasets, resulting in a network that highlights key dependencies among economic indicators.

Contribution

It presents a new approach for modeling macro-economic variables through symbolic regression, revealing interaction networks that identify relevant economic dependencies.

Findings

Identified key dependencies among macro-economic indicators.

Developed a variable interaction network for economic data.

Presented detailed models for US help wanted index and CPI inflation.

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.