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Micro data for the macro world

Irving Fisher Committee

No 53 in IFC Bulletins from Bank for International Settlements

Date: 2021 Written 2021-04
ISBN: 978-92-9259-469-5
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Downloads: (external link)
http://www.bis.org/ifc/publ/ifcb53.pdf Full PDF document (application/pdf)
http://www.bis.org/ifc/publ/ifcb53.htm (text/html)

Chapters in this book:

Best practise recommendations on facilitating access to microdata: outcomes from the INEXDA working group on data access Downloads
Stefan Bender
Collect once, use multiple times – the Reserve Bank of New Zealand's data collection approach Downloads
Steffi Schuster and Neil Humphries
Cross-border contagion risk to the Malaysian banking system Downloads
Harikumara Sababathy and Lim Sheng Ling
Data management in the data evolution era at Bank of Thailand Downloads
Pimpimol Chansang
Enhancing user knowledge by providing (richer) context to microdata Downloads
Christian Hirsch
Financial behaviour of households under the lenses of distributional aspects: the OeNB's experiences Downloads
Johannes Turner
Granular data offer new opportunities for stress testing Downloads
Cees Ullersma and Iman Lelyveld
Incorporating micro data into macro policy decision-making Downloads
Jean-Marc Israel and Bruno Tissot
Key indicators for sectoral productivity analysis Downloads
Laurent Olislager
Linking micro datasets to better service policy-making and analyses Downloads
Jean-Marc Israel
Measuring aggregate housing wealth: new insights from automated valuation models Downloads
Susan Hume McIntosh
OeNB's reporting data model as RegTech/SupTech solution Downloads
Johannes Turner
Paradigms for modern statistical data frameworks and systems Downloads
Michal Piechocki
Post-crisis skills landscape: the emergence of "Purple People" Downloads
Luís Teles Dias
Predicting and preventing financial crisis – where do we stand? Downloads
Boris Hofmann
Predicting economic growth using machine learning techniques and sentiment analysis Downloads
Berkay Akisoglu
Quality checks on granular banking data: an experimental approach based on machine learning Downloads
Fabio Zambuto

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