Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States

Vincent A. Schmidt*, Jane M. Binner, Richard G. Anderson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

This paper demonstrates a mechanism whereby rules can be extracted from a feedforward neural network trained to characterize the inflation "pass-through" problem in American monetary policy, defined as the relationship between changes in the growth rate(s) of individual commodities and the economy-wide rate of growth of consumer prices. Monthly price data are encoded and used to train a group of candidate connectionist architectures. One candidate is selected for rule extraction, using a custom decompositional extraction algorithm that generates rules in human-readable and machine-executable form. Rule and network accuracy are compared, and comments are made on the relationships expressed within the discovered rules. The types of discovered relationships could be used to guide monetary policy decisions.

Original languageEnglish
Title of host publicationProceedings of the 2010 International Conference on Artificial Intelligence, ICAI 2010
EditorsHamid R. Arabnia, David de la Fuente, Elena B. Kozerenko, et al
PublisherCSREA
Pages318-324
Number of pages7
ISBN (Print)978-1-6013-2148-0
Publication statusPublished - 1 Dec 2010
Event2010 International Conference on Artificial Intelligence - Las Vegas, NV, United States
Duration: 12 Jul 201015 Jul 2010

Conference

Conference2010 International Conference on Artificial Intelligence
Abbreviated titleIC-AI 2010
CountryUnited States
CityLas Vegas, NV
Period12/07/1015/07/10

Keywords

  • consumer prices
  • data mining
  • inflation
  • neural network
  • rule generation

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    Schmidt, V. A., Binner, J. M., & Anderson, R. G. (2010). Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States. In H. R. Arabnia, D. de la Fuente, E. B. Kozerenko, & et al (Eds.), Proceedings of the 2010 International Conference on Artificial Intelligence, ICAI 2010 (pp. 318-324). CSREA.