Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this paper, we investigate different methods of assessing business cycles for the European Union in general and the euro area in particular. First, we conduct a Monte Carlo experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analyzed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, we used the structural model’s parameters calibrated to the euro area’s real GDP and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models consisting of popular Hodrick-Prescott, Christiano-Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. We find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and business cycles; and (iii) the best-performing Monte Carlo approaches provide a reasonable combination as the suite of models. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the business cycle for real GDP. Second, we estimate the business cycles for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. Our analysis also confirms that the business cycles of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronised with the aggregate euro area.
JEL Codes: C31, E27, E32.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
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