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Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero

, organizational, and budgetary challenges . The success of big data projects lies not in implementing a particular piece of technology, but rather in establishing an environment of people and processes that take big data innovations forward and put them to work. Given the diverse skills needed to deal with big data, it also provides an opportunity for organizations to break their internal silos, including between users and producers of data and statistics . From individual applications of big data to its incorporation into the systematic, regular, and large

Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero

well as distilling the most important elements for macroeconomic and financial analyses. 15. Although the three features may directly (1 and 2) or indirectly (3) provide valuable data for policy analysis, they are interlinked and as such cannot be completely separated . This paper provides examples but does not intend to give a comprehensive big data project inventory. 4 It rather seeks to illustrate how big data projects can potentially enhance policy analysis. However, moving forward, further exploration and proof of concepts are required to ensure their

Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero
Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.
International Monetary Fund. Statistics Dept.

part) or summaries thereof to parties outside the IMF other than agencies or instrumentalities of the TA recipient, World Bank staff, other technical assistance providers and donors with legitimate interest, shall require the explicit consent of the TA recipient and the IMF’s Statistics Department. Contents Glossary SUMMARY OF MISSION OUTCOMES AND PRIORITY RECOMMENDATIONS DETAILED TECHNICAL ASSESSMENT AND RECOMMENDATIONS A. Action Plan B. Introduction C. Residential Property Price Index D. The Big Data Project E. The Commercial Property

International Monetary Fund. Statistics Dept.

data to more broadly represent the structure of the market. The BI should continue the work on developing an alternative RPPI using Big Data (listings) from real estate web portals in response to challenges to the current RPPI . The Big Data project offers considerable potential for the compilation of robust asking price indexes for the secondary market, following a hedonic methodology. This is particularly the case given the unavailability of suitable price observation data from administrative sources. The BI should continue to follow the survey based approach for

deployment of big data projects (quality and governance aspects). These portend a better future but also raise fears of further widening the gap between the haves and the have-nots. Perhaps what is missing in this big picture is how the focus of policies can be changed from external values (such as competition, consumption, and profits) to internal values (such as cooperation, compassion, and happiness). The articles in this edition shed light on these issues and how, in the future, economic policies need to evolve to balance tradeoffs and be more supportive and inclusive

International Monetary Fund. Statistics Dept.

policy makers to inform monetary policy and inflation targeting. The use of RPPIs as an input to the national accounts is also an important, although secondary, application. 3. The GSO should continue the current data collection initiatives, the survey of property developers and the big data project on property listings (advertisements) in the short term, before making an informed selection of the most suitable data source within the next 12 months . The assessment underpinning the selection of the most suitable data source will require around 9–12 months of data and

International Monetary Fund. Statistics Dept.
A technical assistance (TA) mission was conducted during July 9–13, 2018 to assist the General Statistics Office of Vietnam (GSO) with the development of a residential property price index (RPPI). This was the first mission conducted to Vietnam under the auspices of the multi-annual STA Data for Decisions (D4D) trust fund. The main objective of TA provided to Vietnam under the D4D will be to assist the GSO to develop an RPPI. The GSO recently launched two initiatives to collect potential source data for the RPPI since taxation data are unreliable in respect of reported transaction prices, and the State Bank of Vietnam (SBV) does not collect loan level mortgage data.
International Monetary Fund. Strategy, Policy, & and Review Department

roads to track GDP growth. The Netherlands also uses Facebook and Twitter data to estimate consumer sentiment. Along with Google and Data provider, it published a Big Data study to measure the size of the Dutch digital economy. Eurostat has partnered with European statistical offices to pilot Big Data projects and uses Big Data for macroeconomic nowcasting. Eurostat , in collaboration with the University of Bergamo, used the number of Wikipedia page views to predict tourism flows to three European cities. Central banks and other agencies also use Big Data for