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- Publisher
- Springer Journals
- Copyright
- Copyright
- Subject
- Economics; Economic Theory/Quantitative Economics/Mathematical Methods; Computer Appl. in Social and Behavioral Sciences; Operations Research/Decision Theory; Behavioral/Experimental Economics; Math Applications in Computer Science
- ISSN
- 0927-7099
- eISSN
- 1572-9974
- DOI
- 10.1007/BF00115691
- Publisher site
- See Article on Publisher Site
Abstract
In the economics literature on exchange rate determination no theory has yet been found that performs well in out-of-sample prediction experiments. Until today the simple random walk model has never been significantly outperformed. We have identified a set of fundamental long-run exchange rate models from literature that are well-known among economists. This paper investigates whether a neural network representation of these structural exchange rate models improves the out-of-sample prediction performance of the linear versions. Empirical results are reported in the case of the US dollar-Deutsche Mark exchange rate.
Journal
Computational Economics – Springer Journals
Published: May 14, 2004