By Andrew Kindman
This research proposes and tests several novel strategies for enhancing the strength of conventional, signal-based currency crisis Early Warning Systems (EWS). Using country level, monthly macroeconomic time-series data, it develops an algorithmic process for identifying periods of elevated currency crisis risk and achieves robust results. The proposed changes to current EWS include: 1) an adjustment to the process by which crises are identified empirically, 2) the addition of control panels to dampen the prevalence of false positives, 3) the addition of inter-temporal interaction terms that strive to bring the forecasting model in line with contemporary theoretical models of currency crisis, and 4) the addition of an algorithm for controlling post-crisis bias in macroeconomic trends. In out-of-sample, post-estimation analysis, the system is able to identify 75% of crisis incidents while generating false positives at a rate of less than 20%. Currency crisis EWS have challenged economists for some time, and though these results are not directly comparable to current EWS based on differences in reporting strategies, they are strong enough to warrant further investigation, particularly for applicability as policy instruments.
Advisor: Charles Becker, Kent Kimbrough
