This paper uses several macroeconomic and financial indicators within a Markov Switching (MS) framework to predict the turning points of the business cycle. The presented model is applied to monthly German real-time data covering the recession and the recovery after the financial crisis. We show...

The detection of business-cycle turning points is usually performed with non-linear discrete-regime models such as binary dependent variable (e.g., probit or logit) or Markov-switching methods. The probit model has the drawback that the continuous underlying target variable is discretized, with...

We study the forecasting performance of three alternative large scale approaches for German key macroeconomic variables using a dataset that consists of 123 variables in quarterly frequency. These three approaches handle the dimensionality problem evoked by such a large dataset by aggregating...