Data on Google searches help predict the unemployment rate in the U.S. But the predictive power of Google searches is limited to short-term predictions, the value of Google data for forecasting purposes is episodic, and the improvements in forecasting accuracy are only modest. The results,...

In this paper a Bayesian vector autoregressive model for nowcasting the seasonally non-adjusted unemployment rate in EU-countries is developed. On top of the official statistical releases, the model utilizes Google search data and the effect of Google data on the forecasting performance of the...

There are over 3 billion searches globally on Google every day. This report examines whether Google search queries can be used to predict the present and the near future unemployment rate in Finland. Predicting the present and the near future is of interest, as the official records of the state...

In this report we document the ETLAnow project. ETLAnow is a model for forecasting with big data. At the moment, it predicts the unemployment rate in the EU-28 countries using Google search data. This document is subject to updates as the ETLAnow project advances.

Die demographische Alterung tritt in Deutschland derzeit in akute Phase ein, die insbesondere in den gesetzlichen Sozialversicherungen zu finanziellen Anspannungen führt und mittelbar auch Risiken für die Entwicklung des Bundeshaushalts erzeugt. Dies wird im vorliegenden Bericht mit Hilfe von...

Die demographische Alterung tritt in Deutschland aktuell in eine akute Phase, die unter dem derzeit geltenden Recht zu großen finanziellen Anspannungen in den gesetzlichen Sozialversicherungen führt und mittelbar auch Risiken für die Entwicklung des Bundeshaushalts erzeugt. Dies wird im...

The authors analyse the macroeconomic impact of the French work-sharing reform of 2000 (a reduction of standard working hours in combination with wage subsidies). Using a vector error correction model (VECM) for several labour market variables as well as inflation and output the authors produce...

We suggest the use of an Internet job-search indicator (the Google Index, GI) as the best leading indicator to predict the US unemployment rate. We perform a deep out-of-sample forecasting comparison analyzing many models that adopt both our preferred leading indicator (GI), the more standard...

Using U.S. real-time data, we show that changes in the unemployment rate unexplained by Okun's Law have significant predictive power for GDP data revisions. A positive (negative) error in Okun's Law in real time implies that GDP will be later revised to show less (more) growth than initially...

We integrate the housing market and the labor market in a dynamic general equilibrium model with credit and search frictions. The model is confronted with the U.S. macroeconomic time series. Our estimated model can account for two prominent facts observed in the data. First, the land price and...