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

. Big data have the potential to help address development challenges and meet demands for compiling Sustainable Development Goals (SDG) indicators, such as gender equality. 6 The private company LinkedIn is already using its granular data to publish gender diversity statistics and provide training on gender statistics ( Karani 2017 ). 19. Following the IMF Big Data and Analytics Symposium, the IMF in-house Big Data Innovation Challenge paved the way for innovative ways to leverage big data in future work of the IMF . The top six ideas were approved for proof of

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.
Mr. Jean-Francois Dauphin, Mr. Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Mrs. Nujin Suphaphiphat, Yifei Wang, and Hanqi Zhang

Data Sampling ML Machine Learning NN Neural Network OECD Organization for Economic Co-operation and Development OLS Ordinary Least Squares ReLU Rectifier Linear Unit RF Random Forest RMSE Root Mean Squared Error RNN Recursive Neural Networks SVM Support Vector Machine VAR Vector Autoregressive Model WEI Weekly Economic Index 1 The author(s) would like to thank participants of the IMF Big Data Talks and European Department seminars for suggestions. All errors and

Samuel P. Fraiberger, Dongyeol Lee, Mr. Damien Puy, and Mr. Romain Ranciere

a Newey and West (1987) estimator. Figure 9 Benchmark results – Country and Time Split Note : t denotes the number of days. The blue line reports the cumulative response of equity prices to local news sentiment shocks. The green line reports the cumulative response to global news sentiment shocks. Standard errors are always corrected for serial auto-correlation and heteroskedasticity using a Newey and West (1987) estimator. 1 We are grateful to the IMF Big data initiative for financial support, as well as participants in the CEPR

Samuel P. Fraiberger, Dongyeol Lee, Mr. Damien Puy, and Mr. Romain Ranciere
We assess the impact of media sentiment on international equity prices using more than 4.5 million Reuters articles published across the globe between 1991 and 2015. News sentiment robustly predicts daily returns in both advanced and emerging markets, even after controlling for known determinants of stock prices. But not all news-sentiment is alike. A local (country-specific) increase in news optimism (pessimism) predicts a small and transitory increase (decrease) in local returns. By contrast, changes in global news sentiment have a larger impact on equity returns around the world, which does not reverse in the short run. We also find evidence that news sentiment affects mainly foreign – rather than local – investors: although local news optimism attracts international equity flows for a few days, global news optimism generates a permanent foreign equity inflow. Our results confirm the value of media content in capturing investor sentiment.
Mr. Jean-Francois Dauphin, Mr. Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Mrs. Nujin Suphaphiphat, Yifei Wang, and Hanqi Zhang
This paper describes recent work to strengthen nowcasting capacity at the IMF’s European department. It motivates and compiles datasets of standard and nontraditional variables, such as Google search and air quality. It applies standard dynamic factor models (DFMs) and several machine learning (ML) algorithms to nowcast GDP growth across a heterogenous group of European economies during normal and crisis times. Most of our methods significantly outperform the AR(1) benchmark model. Our DFMs tend to perform better during normal times while many of the ML methods we used performed strongly at identifying turning points. Our approach is easily applicable to other countries, subject to data availability.