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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.
Abstract:In this paper, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and BadenWürttemberg) and Eastern Germany. We overcome the problem of a 'data-poor environment' at the sub-national level by complementing various regional indicators with more than 200 national and international indicators. We calculate singleindicator, multi-indicator, pooled and factor forecasts in a pseudo real-time setting. Our results show that we can significantly increase forecast accuracy compared to an autoregressive benchmark model, both for short and long term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP.
Over the last decade, the topic of regional economic forecasting has become increasingly prevalent in academic literature. The most striking problem in this context is data availability at a regional level. However, considerable methodological improvements have been made to address this problem. This paper summarises a multitude of articles from academic journals and describes state-of-the-art techniques in regional economic forecasting. After identifying current practices, the article closes with a roadmap for possible future research activities.
Abstract. 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 and the history of a journal's ranking performance. Most other rankings that are commonly applied neglect this fact. The Elo ranking methodology 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 data set consists of more than 20 000 journals from many scientific fields for the period from 1999 to 2015. We show that the Elo approach produces similar but by no means identical rankings compared to other rankings based on the SNIP alone or the Tournament Method. Especially the rank order for rather 'middle-class' journals can tremendously change.
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