Modeling the business and financial cycle in a multivariate structural time series model

We consider a multivariate unobserved component time series model to disentangle the short-term and medium-term cycle for the G7 countries and the Netherlands using four key macroeconomic and financial time series. The novel aspect of our approach is that we simultaneously decompose the short-term and medium-term dynamics of these variables by means of a combination of their estimated cycles. Our results show that the cyclical movements of credit volumes and house prices are mostly driven by the medium-term cycle, while the macroeconomic variables are equally driven by the short-term and medium-term cycle. For most countries, the co-movement between the cycles of the financial and macroeconomic variables is mainly present in the medium-term. First, we find strong co-cyclicality between the medium-term cycles of house prices and GDP in all countries we analyzed. Second, the relation between the medium-term cycles of GDP and credit is more complex. We find strong concordance between both cycles in only three countries. However, in three other countries we find ˜indirect concordance, i.e. the medium-term cycles of credit and house prices share co-cyclicality, while in turn the medium-term cycles of house prices and GDP share commonality. This outcome might indicate that the house price cycle is “at least partly“ driven by the credit cycle. Lastly, the cross-country concordance of both the short-term cycles and the medium-term cycles of GDP, house prices and credit is low. Hence, the bulk of the cyclical movements seem to be driven by domestic rather than global factors.
 
Keywords: unobserved component time series model, Kalman filter, maximum likelihood estimation, short-term and medium-term cycles. 
JEL classifications: C32, E32, G01.

 

Working paper no. 573