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 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...

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...