Estimating the Business Cycle of Ukraine Under the Conditions of Large External Compound Shocks

Abstract

Abstract. Introduction. Conventional business cycle estimation methods typically rely on the assumption that the shocks to an economy are normally distributed. However, global social and political instability can result in external shocks that are more severe than purely economic shocks, thus hampering the ability of these estimation methods to separate cyclical behavior from long-run dynamics. Since effective economic policy is dependent upon the ability to make accurate forecasts, an understanding of the properties of business cycle estimation methods in the presence of large shocks in the data is of first order importance. Purpose. The purpose of this article was to compare the ability of various business cycle estimation methods — and in particular detrending filters — to account for large external compound shocks (i.e. those containing both economic and noneconomic components) when extracting the cyclical component of the real GDP series of Ukraine. In view of Ukraine’s policy of European integration, a secondary goal was to investigate the performance of various detrending filters in deriving a measure of business cycle co-movement of Ukraine vis-à-vis the EU that is robust to external compound shocks. Results. Using an unobserved components model with external geopolitical shocks as a benchmark, it was found that the application of the boosted Hodrick-Prescott filter, the Christiano-Fitzgerald filter, and the Hamilton regression filter to the real GDP data of Ukraine produced cyclical components that exhibited spurious dynamics, particularly from 2020 and onward. Conclusions. It was shown that no single business cycle estimation method performed the best in application to the real GDP series of Ukraine. While the unobserved components model relied on extensive researcher-specified assumptions and an ad hoc approach to identifying external compound shocks, the use of data filtering resulted in series that did not accurately reflect cyclical and trend dynamics in Ukraine toward the end of the sample. Thus, there is a practical need to develop new business cycle estimation models that would be able to account for the distorting influence of large external compound shocks.