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A neural network approach to long-run exchange rate prediction. (English) Zbl 0846.90027

Summary: 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.

MSC:

91B84 Economic time series analysis
62P20 Applications of statistics to economics
92B20 Neural networks for/in biological studies, artificial life and related topics
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