Page 138 - Invited Paper Session (IPS) - Volume 2

IPS188 Bruno Tissot
17. Loberto M, A Luciani and M Pangallo (2018): “The potential of big
housing data: an application to the Italian real-estate market”, Bank of
Italy Temi di Discussione, no 1171, April.
18. Meeting of the Expert Group on International Statistical Classifications
(2015): Classification of Types of Big Data, United Nations Department of
Economic and Social Affairs, ESA/STAT/AC.289/26, May.
19. Meng, X (2014): “A trio of inference problems that could win you a
Nobel Prize in statistics (if you help fund it)”, in X Lin, C Genest, D Banks,
G Molenberghs, D Scott and J-L Wang (eds), Past, present, and future of
statistical science, Chapman and Hall, pp 537–62.
20. Modugno, M (2011): “Nowcasting inflation using high frequency data”,
ECB Working Paper Series, no 1324, April.
21. Reinsdorf M and P Schreyer (2019): “Measuring consumer inflation in a
digital economy”, OECD Statistics and Data Directorate Working Paper,
No 101, February.
22. Rigobon, R (2018): “Promise: measuring from inflation to discrimination”,
presentation given at the workshop on “Big data for central bank
policies”, Bank Indonesia, Bali, 23–25 July.
23. The Economist (2017): “The world’s most valuable resource is no longer
oil, but data”, 6 May edition.
24. Tissot, B (2014): “Monitoring house prices from a financial stability
perspective - the BIS experience”, International Statistical Institute
Regional Statistics Conference, November.
25. ——— (2018): “Providing comparable information to assess global
financial stability risks”, Eurostat Statistical Reports, KS-FT-18-001,
January.
26. ——— (2019): “Making the most of big data for financial stability
purposes”, in S Strydom and M Strydom (eds), Big Data Governance and
Perspectives in Knowledge Management, IGI Global, pp 1–24.
27. Wibisono O and A Zulen (2019): “Measuring stakeholders' expectations
for the central bank's policy rate”, IFC Bulletin, no 50, May.

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