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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.
Marijn A. Bolhuis and Brett Rayner
We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast errors than traditional models. By combining multiple machine learning models into ensembles, we lower forecast errors even further. We also identify measures of variable importance to help improve the transparency of machine learning-based forecasts. Applying the framework to Turkey reduces forecast errors by at least 30 percent relative to traditional models. The framework also better predicts economic volatility, suggesting that machine learning techniques could be an important part of the macro forecasting toolkit of many countries.
Marijn A. Bolhuis and Brett Rayner

Statistical Learning: Data Mining, Inference, and Prediction,” Springer Science & Business Media . James , G. , Witten , D. , Hastie , T. , & Tibshirani , R. , 2013 . “ An Introduction to Statistical Learning ,” (Vol. 112 , p. 18 ). New York : Springer . Jung , J. K. , Patnam , M. , & Ter-Martirosyan , A. , 2018 . “ An Algorithmic Crystal Ball: Forecasts Based on Machine Learning ,” IMF Working Paper 18/230 . Kim , H. H. , & Swanson , N. R. , 2018 . “ Mining Big Data Using Parsimonious Factor, Machine Learning, Variable

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

modernizing statistical production processes. 32. Compilers are piloting big data projects mostly to refine and complement traditional data sources . Some well-documented examples of big data use by official compilers are 17 (1) mobile phone data for tourism, transportation, and urban statistics (for example, Eurostat, Belgium, Brazil, Indonesia, Israel, Italy, World Bank in Nigeria, Poland); (2) web scraping for price indices, labor market indicators, and enterprise profiling (Eurostat, China, Ecuador, Finland, Germany, Hungary, Japan); (3) smart meters for energy and

International Monetary Fund. Asia and Pacific Dept

communication on monetary policy and financial stability. Statistics Use of big data for timely economic monitoring could support prompt policy decision making. The Fund is providing technical assistance on the use of big data to develop an enhanced residential property price index. The Fund also participated in a pilot project on big data using scanner data to enhance private consumption and consumer price statistics. Cash management Improve cash management. The Fund is providing technical assistance to improve cash management, which could also support

International Monetary Fund. Asia and Pacific Dept
This 2019 Article IV Consultation discusses that the Indonesian economy performed well in 2018, despite external headwinds, including capital flow reversals. Growth stabilized above 5 percent and inflation eased to around 3 percent. A surge in imports and weak export growth contributed to a higher current account deficit. Growth is projected to remain stable over the medium term. Inflation is expected to remain within the target band and the current account deficit is expected to narrow gradually on lower imports. Risks are tilted to the downside and are mainly external. Reliance on portfolio inflows to finance the twin deficits leaves Indonesia vulnerable to capital flow reversals. Creating quality jobs for the young and growing population to harness Indonesia’s demographic dividend requires a stronger impetus to growth, which has been constrained by structural weaknesses, including low tax revenues, shallow financial markets, and labor and product market rigidities.