Time‐Varying Dynamics of the German Business Cycle: A Comprehensive Investigation*

Abstract

This paper provides insights into the time-varying dynamics of the German business cycle over the last five decades. To do so, I employ an open-economy time-varying parameter VAR with stochastic volatility, which I estimate by quasi-Bayesian techniques. The reduced-form analysis reveals substantial shifts in the variables' longrun growth rates and shock volatilities over time. German trend inflation has strongly decreased and settled at a historically low level. GDP growth volatility exhibits marked fluctuations over time and has dropped to historically low levels only after the global financial crisis. The structural analysis employs externally identified oil supply shocks along with a recursive identification scheme to identify key macroeconomic shocks. The analysis reveals strong fluctuations in both the impact responses of macroeconomic aggregates to these shocks and the shock propagation processes. Thus, I conclude that business cycle stabilization in Germany is driven by both good policy and good luck.

Quarterly GDP Estimates for the German States: New Data for Business Cycle Analyses and Long-Run Dynamics

“…The last step is to set a definition on how the variance-covariancematrix of the VAR, Σ t , looks like. The recent literature on the dynamics of the German business cycle is strongly in favor of changes in the volatility and thus allowing for a heteroscedastic error structure (Reif, 2022). We follow Koop et al (2020c) and apply the stochastic volatility specification of Cogley and Sargent (2005) and Carriero et al (2016):…”

“…SV by additional macroeconomic and state-level variables that might explain quarterly GDP growth. In line with Koop et al (2020c); Reif (2022); Schorfheide and Song (2015) we select the following four macroeconomic variables for Germany: the seasonally-adjusted consumer price index, the bank rate, the exchange rate, and the oil price (see the Supplementary Material for more details on the indicators). With the exception of the bank rate that enters the model in quarterly first differences, all other macroeconomic series are transformed into quarterly log-differences.…”

Measuring the degree of connection between currency futures: Empirical dive into higher moments

“…Second, this method is robust to the misspecification of the system in case of the presence of a structural break. Even if the parameter instability exists, the technique is flexible enough to deal with it (Reif, 2022). Our analysis includes more than 20 years of daily rolling estimates of volatility, skewness and kurtosis for the most liquid and traded futures quotes at the Chicago Mercantile Exchange (CME).…”

Structural dynamic shifts in the Russian macroeconomics in the period of global challenges

“…Despite the higher standard of living in developed countries, their economies can be subject to shocks. Thus, (Reif, 2022) noted that the German economy is sensitive to oil shocks, a high export component and high energy intensity of production. (Borumand et al, 2019) noted the influence of oil price, exchange rate and inflation rate factors on the Iranian economy.…”