High frequency data in DBnomics

Dear all,

To follow the evolution of economic activity in real time, especially in times of crisis, high frequency data may be useful, either from existing sources or to be built (for example using webscraping to download data from Internet and construct series linked to activity, pollution, trade, housing markets or transportation).

To add more high frequency data in DBnomics, several challenges have been launched on www.b2ideas.eu, a platform created to improve interactions between professionals and students.

These challenges aim at searching at existing high frequency data from (special) agencies, and also at building new series. These relate to exchanges by boat, by plane or by trucks, to housing, and to sectoral activity in hotels and restaurants. These challenges have been launched by DBnomics and several departments from Banque de France.

Both students and professionals can participate, to help improving the tracking of daily / weekly activity and fuel more series in DBnomics.

If you have questions on these challenges (or if you want to launch another one, on this topic or any other), feel free to contact B2ideas at the following address: info@b2ideas.eu

Best regards,


Sounds great, thanks!

This is a great initiative, thank you! I am working on it.

In the Covid Economics journal from the CEPR (https://cepr.org/content/covid-economics-vetted-and-real-time-papers-0#PreviousIssues), issue 6, there is an article by Lewis, Mertens and Stock where they compute an index of the US weekly activity. Table below list the series they use to construct the index.

Thanks for the information. We take good notice of it! Much appreciated, Jean-Charles