This paper proposes a new model-based method to obtain a coincident indicator for the business cycle. A dynamic factor model with trend components and a common cycle component is considered which can be estimated using standard maximum likelihood methods. The multivariate unobserved components...

This study introduces a monthly coincident indicator for consumption in Germany based on Google Trends data on web search activity. In real-time nowcasting experiments the indicator outperforms common survey-based indicators in predicting consumption. Unlike those indicators, it provides...

This study introduces a monthly coincident indicator for consumption in Germany based on Google Trends data on web search activity. In real-time nowcasting experiments the indicator outperforms common survey-based indicators in predicting consumption. Unlike those indicators, it provides...

This paper offers two innovations for empirical growth research. First, the paper discusses principal components augmented regressions to take into account all available information in well-behaved regressions. Second, the paper proposes a frequentist model averaging framework as an alternative...

This paper discusses a large-scale factor model for the German economy. Following the recent literature, a data set of 121 time series is used via principal component analysis to determine the factors, which enter a dynamic model for German GDP. The model is compared with alternative univariate...

Factor models can cope with many variables without running into scarce degrees of freedom problems often faced in a regression-based analysis. In this article we review recent work on dynamic factor models that have become popular in macroeconomic policy analysis and forecasting. By means of an...