The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
SVM sensitivity analysis: an application to currency crises aftermaths
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Data strip mining for the virtual design of pharmaceuticals with neural networks
IEEE Transactions on Neural Networks
SOM-based data analysis of speculative attacks' real effects
Intelligent Data Analysis
Empirical Analysis Of Speculative Attacks With Contractionary Real Effects
International Journal of Intelligent Systems in Accounting and Finance Management
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The effects of a currency crisis on a country's economy depend on non-linear relations among several variables that characterize the economic, financial, legal, and socio-political structure of the country at the onset of the crisis. We seek to determine which variables are significant in explaining currency crises' real effects when they are all considered together. This paper uses a novel algorithm with Partial Least Squares (PLS) for selecting relevant variables. This algorithm works well with datasets characterized by few observations relative to the number of right-hand side variables and nonlinearity. Variables describing the banking sector, the international trade, the severity of the crisis, and foreign interest rates are found to be significant. On the other hand, socio-political variables, IMF's intervention, and legal variables are found to be less significant. Our algorithm's results are compared with all-best subsets variable selection and their predictive power is examined using neural networks.