Bivariate SVAR models employing long-run identifying restrictions are often used to investigate the source of business cycle fluctuations. Their advantage is the simplicity in use and interpretation. However, their low dimension may also lead to a failure of the identification procedure, with...

We examine the comovements between the output indexes of three German sectors (manufacturing, mining, and agriculture) and the three corresponding sectoral stock market indexes. It is found that data with and without seasonal adjustment give mixed results on the long-run interaction between the...

James Hamilton put doubt on the quality of the HP filter estimates, and proposed an alternative regression approach to decompose trend and cycle of time series (H filter). We investigate the new H filter in detail and compare it to the HP filter. We apply both to German GDP time series. We find...

Based on monthly data covering the period from 1987 to 2019, we analyse whether cross-sectional moments of stock market returns may provide information about the future position of the German business cycle. We apply in-sample forecasting regressions with and without leading indicators as...

We examine the comovements between the output indexes of three German sectors (manufacturing, mining, and agriculture) and the three corresponding sectoral stock market indexes. It is found that data with and without seasonal adjustment give mixed results on the long-run interaction between the...

In this paper, we make multi-step forecasts of the monthly growth rates of the prices and rents for flats in 26 largest German cities. Given the small time dimension, the forecasts are done in a panel-data format. In addition, we use panel models that account for spatial dependence between the...