Big data conversion techniques including their main features and characteristics
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, techniques to convert unstructured big data to structured time series suitable for nowcasting purposes are considered. Several empirical examples which illustrate the potential of big data in economics are included. Finally, a practical application based on textual data analysis is provided, where a huge set of about 3 million news articles for the construction of an economic uncertainty indicator is exploited.
In March this year a first ESTP course was offered by Eurostat to the ESS members, and provided a great opportunity to really dig into and discuss how we manage our projects and how we can work together better. The course lasted three days during which the 19 attendees went through the entire project lifecycle, from conception to conclusion, with a good look at the benefits and sustainability of the project. One overall feedback was about the usefulness of the Commission's Methodology, PM2, which had just launched its Open Edition and is now freely available to all.
The course highlighted differences in the degree of usage of project management good practices showing the gap that still needs to be filled to work together with utmost efficiency. This is also reflected in the participants' equest for a follow-up advanced course to further develop their practical knowledge of project management. Building on this years' experience, Eurostat is considering repeating the course – and – if there is sufficient demand – adding an extra day to discuss the Agile approach to project management. Agile project management is mainly used in IT projects (software development) – but not only: any project that has to deliver concrete results in a very short period of time can benefit from using some Agile techniques. However, with no demand, there is no offer – all ESS members are hereby invited to express their interest in having a one-day Agile add-on to the course. The add-on can be taken together with the course or as a stand-alone course. If you are interested in this course add-on, please send an e-mail to the course leader.
Release of JDemetra+ version 2.2 as software officially recommended for the seasonal and calendar adjustment of official statistics in the EU
The Seasonal Adjustment Expert Group decided that JDemetra+ version 2.2 will be recommended, as of July 2017, as software for conducting seasonal adjustment of official statistics. This new version, which runs under Java 8, answers to several wishes expressed by the Seasonal Adjustment User Group since the release of JDemetra+ version 2.0.
JDemetra+ is a new tool for seasonal adjustment (SA) developed by the National Bank of Belgium (NBB) in cooperation with the Deutsche Bundesbank and Eurostat in accordance with the Guidelines of the European Statistical System (ESS). JDemetra+ has been officially recommended, since 2 February 2015, to the members of the ESS and the European System of Central Banks as software for seasonal and calendar adjustment of official statistics.
JDemetra+ implements the concepts and algorithms used in the two leading SA methods: TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS. Those methods have been re-engineered using an object-oriented approach that enables easier handling, extensions and modifications. Besides seasonal adjustment, JDemetra+ bundles other time series models that are useful in the production or analysis of economic statistics, including for instance outlier detection, nowcasting, temporal disaggregation or benchmarking.
EURONA is an open access, peer-reviewed, scholarly journal dedicated to National Accounts and Macroeconomic Indicators. EURONA aims at providing a platform for researchers,scholars, producers and users of macroeconomic statistics to exchange their research findings.
The first EURONA issue of 2017 contains the following articles:
"Output growth and inflation across space and time", by W. Erwin Diewert and Kevin J. Fox
"Uses of national accounts from the 17th century till present and three suggestions for the future", by Frits Bos
"Processing scanner data by an augmented GUV index", by Ludwig von Auer
"Big data types for macroeconomic nowcasting", by Dario Buono, Gian Luigi Mazzi, George Kapetanios, Massimiliano Marcellino and Fotis Papailias