Paper Info
Title
Macro-economic time series modeling and interaction networks
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.
Year
DOI
Venue
2011
10.1007/978-3-642-20520-0_11
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Keywords
DocType
Volume
interesting dependency,macro-economic time series modeling,macro-economic dataset,interesting model,important economic indicator,variable interaction network,economic quantity,macro-economic indicator,genetic programming,finance,variable interaction,interaction network,econometrics.,time series model,economic indicator,econometrics,economic model
Conference
abs/1212.2044
ISSN
Citations 
PageRank 
Applications of Evolutionary Computation, LNCS 6625 (Springer Berlin Heidelberg), pp. 101-110 (2011)
3
0.52
References 
Authors
6
4
Name
Order
Citations
PageRank
Gabriel Kronberger119225.40
Stefan Fink230.86
Michael Kommenda39715.58
Michael Affenzeller433962.47