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HomeBig data conversion techniques including their main features and characteristics
2017 edition
Big data conversion techniques including their main features and characteristics
2017 edition
Publication metadata
Big data have high potential for nowcasting and forecasting economic variables. However, they are often unstructured so that there is a need to transform them into a limited number of time series which efficiently summarise the relevant information for nowcasting or short term forecasting the economic indicators of interest. Data structuring and conversion is a difficult task, as the researcher is called to translate the unstructured data and summarise them into a format which is both meaningful and informative for the nowcasting exercise. In this paper we consider techniques to convert unstructured big data to structured time series suitable for nowcasting purposes. We also include several empirical examples which illustrate the potential of big data in economics. Finally, we provide a practical application based on textual data analysis, where we exploit a huge set of about 3 million news articles for the construction of an economic uncertainty indicator.
- Published:
- Corporate author(s): Eurostat (European Commission)
- Personal author(s): Marcellino, Massimiliano ; Kapetanios, George ; Papailias, Fotis
- Themes: Economy — Finance , Economy and finance
- Subject: data collection , data processing , economic forecasting , economic indicator , economics , report , statistical method
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| ISSN 2315-0807 | ISBN 978-92-79-70523-6 | DOI 10.2785/461700 | Catalogue number KS-TC-17-003-EN-N |
- Released on EU Publications:
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