Is it time for action(?): Loss minimization in crisis prediction

This thesis aims to design early warning systems, which predict currency, banking and debt crises, and determine the optimum threshold value to be applied on the prediction probabilities for determining the state of the economy as either tranquil, pre-crisis, or adjustment so as to minimize the loss of the economy. In predicting crises by using early warning systems, there exist two potential sources of loss for the economy: Missing a crisis and a false alarm. These sources are called as Type-1 and Type-2 errors respectively. In this study, after designing early warning system that predicts the status of the economy, a loss function is defined to calculate the loss, which arises due to the mentioned errors that might exist in the early warning system. This loss function takes the policy maker as an exogenous decision maker. This study, is not only constructing an early warning system for crisis prediction, but also providing the policy maker with an optimal threshold level for the predictions in order to obtain the optimum early warning system for both developing and developed countries. The data are taken from World Bank, IMF and OECD and span the years between 1980 and 2012. Multinomial logistic regression is used for crisis prediction. As an advantage, it prevents the 'post-crisis bias' problem; by this way the robustness of the analysis is also improved. The multinomial logistic regression is run for two different time windows 't-1, t, t+1' and 't, t+1, t+2' as t denoting the current year. With a threshold level of 20%, the system predicts 60% of the crises correctly for the time window of 't-1, t, t+1', whereas this number increases to 92% for the time window of 't, t+1, t+2'. In calculating the loss function, the threshold level to be applied on the predictions is swept from 0.01 to 1 (1% to 100%). Depending on the literature, 3 different values have been used for the relative risk aversion of the policy maker, which are ? = 0.2, ? = 0.5, and ? = 0.8. According to the results, the lowest value of loss function is obtained at the highest rate of the policy maker's relative risk aversion and lowest rate of threshold level for both time windows. Depending on the results, it is possible to make the following generalization for policy offer: the policy makers should give more importance to the cost of missing crisis and they should keep the threshold level at a lower rate in order to protect their economies against the loss that may arise due to the potential errors, which may be caused by the early warning system. Keywords: Early Warning Systems, Currency Crisis, Banking Crisis, DebtCrisis, Post-crisis Bias, Multinomial Logistic Regression, Threshold Level, LossFunction, Ideal Early Warning System, Missing Crisis, Sending Wrong Signals, False Alarm