Early Warning System: Logit/Probit introduction for Ukraine

Abstract

Introduction. There have been several crises in the world economy since the end of the last century. The developing economies were ones that have suffered the most considering the level of openness, the weak institutional framework, and the market vulnerability to unpredictable shocks. One of the instruments widely used to prevent crises is the Early Warning System models. Purpose. The paper pursues the goal to develop Early Warning System model using logit/probit regression to determine early warning arguments and their appropriate thresholds for Ukraine. Results. The regression algorithm corresponds to the determination of the dependent binary variable associated with the output gap followed by the selection of independent early warning components. The method of integral composite coincident and leading indicators employs to reproduce the quarterly dynamics of real GDP. 1. The arranged coincident indicator depends on the industrial production, agriculture, construction, and the domestic retail trade. The expanded to monthly data quarterly GDP is exploited for the evaluation of the output gap using multivariate filter and Okun's law definition. The 2% difference between the actual and potential GDP is applied for generating binary data of the output gap. 2. The two components of the integral leading index, the world prices of wheat and Russian gas plus the world price of steel figure out one independent variable of logit/probit regression marked as the world price of raw materials. Another one independent variable is arranged by monitoring the demand-supply gap. 3. The obtained probit results are more statistically significant in comparison to the logit model. The marginal effects are 1% for the world price of raw materials and 3% for the demand-supply gap. Conclusions. Considering the higher marginal grade, between two early warning components the demand-supply gap is more sensitive for predicting crises in Ukraine. In the following study, the other early warning components have to be examined for higher predicting capability.

Financial Early Warning System Model Combining Hybrid Semantic Hierarchy with Group Method of Data Handling Neural Network for Detection of Banks’ Risks

“…Admittedly, no such systems perfectly signal before any crisis occurs. False alarms could be possible [8,9]. Besides, factors such as efficient leading-edge analysis, precrisis scrutiny, and widespread global coordination are essentials [10].…”