Klaus Wohlrabe

Nowcasting Regional GDP: The Case of the Free State of Saxony

Abstract 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 take into account the ragged-edge data structure and consider the different information sets faced by a regional forecaster within each quarter. It appears that regional survey results in particular improve forecasting accuracy. Among the 10% best performing models for the short forecasting horizon, one fourth contain regional indicators. Hard indicators from the German manufacturing sector and the Composite Leading Indicator for Europe also deliver useful information for the prediction of regional GDP in Saxony. Unlike national GDP forecasts, the performance of regional GDP is similar across different information sets within a quarter.

Who is the 'Journal Grand Master'? A new ranking based on the Elo rating system

In this paper we transfer the Elo rating system, which is widely accepted in chess, sports and other disciplines, to rank scientific journals. The advantage of the Elo system is the explicit consideration of the factor time or the history of a journal’s performance. Most other rankings that are commonly applied neglect this fact. The Elo ranking approach can easily be applied to any metric, published on a regular basis, to rank journals. We illustrate the approach using the SNIP indicator based on citation data from Scopus. Our balanced panel consists of 7,748 journals from many scientific fields for the period from 1999 to 2015. We show that the Elo approach produces a similar but not identical ranking compared to other rankings based on the SNIP. Especially the rank order for rather ’middle-class’ journals can tremendously change.

Predictive Ability of Business Cycle Indicators under Test

Summary In this paper we assess the information content of seven widely cited early indicators for the euro area with respect to forecasting area-wide industrial production. To this end, we use various tests that are designed to compare competing forecast models. In addition to the standard Diebold-Mariano test, we employ tests that account for specific problems typically encountered in forecast exercises. Specifically, we pay attention to nested model structures, we alleviate the problem of data snooping arising from multiple pairwise testing, and we analyze the structural stability in the relative forecast performance of one indicator compared to a benchmark model. Moreover, we consider loss functions that overweight forecast errors in booms and recessions to check-whether a specific indicator that appears to be a good choice on average is also preferable in times of economic stress. We find that none of this indicators uniformly dominates all its competitors. The optimal choice rather depends on the specific forecast situation and the loss function of the user. For 1-month forecasts the business climate indicator of the European Commission and the OECD composite leading indicator generally work well, for 6-month forecasts the OECD composite leading indicator performs very good by all criteria, and for 12-month forecasts the FAZ-Euro indicator published by the Frankfurter Allgemeine Zeitung is the only one that can beat the benchmark AR(1) model.