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...

This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether...

We use linear regression models and Bayesian Model Averaging procedure to investigate whether Google search query data can improve the nowcast performance of the monthly nonagricultural unemployment rate for Turkey for the period from January 2005 to January 2012. We show that Google search...

This paper documents GDPNow, a "nowcasting" model for gross domestic product (GDP) growth that synthesizes the "bridge equation" approach relating GDP subcomponents to monthly source data with the factor model approach used by Giannone, Reichlin, and Small (2008). The GDPNow model forecasts GDP...

For the timely detection of business-cycle turning points we suggest to use mediumsized linear systems (subset VARs with automated zero restrictions) to forecast the relevant underlying variables, and to derive the probability of the turning point from the forecast density as the probability...

In the recent literature on nowcasting, the use of the so-called bridge models is advocated. These are simple regression models that use data on mixed frequencies, usually with the lower frequency data serving as dependent variables and the higher frequency data as explanatory variables. In this...

We tackle the nowcasting problem at the regional level using a large set of indicators (regional, national and international) for the years 1998 to 2013. We explicitly use the ragged-edge data structure and consider the different information sets faced by a regional forecaster within each...

We tackle the nowcasting problem at the regional level using a large set of indicators (regional, national and international) for the years 1998 to 2013. We explicitly use the ragged-edge data structure and consider the different information sets faced by a regional forecaster within each...