Conditioning business and financial cycles on multivariate information

We estimate output gaps and financial cycles using a cross-country sample comprising Germany, the United States, and the United Kingdom by extending the approach of Berger, Richter & Wong (2022). Specifically, we apply the trend-cycle decomposition of Beveridge & Nelson (1981) within Bayesian vector autoregression models but select different sets of conditioning variables and shrinkage parameters for output gaps and financial cycles - i. e. credit and property price cycles - in each country. As demonstrated by our cross-country results, this strategy yields more realistic estimates of financial cycle amplitudes while retaining reliable output gap estimates. Our results further indicate that large, positive Beveridge-Nelson-based financial cycles, unlike traditional financial cycles, should not be interpreted as early warning indicators of systemic risk. Instead, they indicate periods of financial overhang that may impose constraints on the broader economy.