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...

Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating...

The paper proposes a data driven adaptive model selection strategy. The selection crite- rion measures economic exante forecasting content by means of trading implied cash flows. Empirical evidence suggests that the proposed strategy is neither exposed to selection bias nor to the risk of...

In this paper we adopt a principal components analysis (PCA) to reduce the dimensionality of the term structure and employ autoregressive models (AR) to forecast principal components which, in turn, are used to forecast swap rates. Arguing in favor of structural variation, we propose data...

The Value-at-Risk calculation reduces the dimensionality of the risk factor space. The main reasons for such simplifications are, e.g., technical efficiency, the logic and statistical appropriateness of the model. In Chapter 2 we present three simple mappings: the mapping on the market index,...

The Google Insights data are a collection of recorded Internet searches for a huge number of the keywords, which are available since January 2004. These searches represent a kind of revealed perceptions of Internet users, which are a (possibly not entirely representative) sample of the general...

This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares...

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...