Abstract
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.