Big Data for Macroeconomic Statistics (BDMS)

Source details

Technical staff and managers from national statistics offices and central banks involved in macroeconomic statistics, nowcasting, and data science projects.
Economists, statisticians, data scientists, and decision-makers overseeing related projects or teams.

Workshop aimed at country officials from national statistics offices (NSOs) and central banks to acquire knowledge and skills to leverage Big Data and advanced technologies for generating high-frequency and detailed statistics to enhance macroeconomic monitoring and research in the region.
Delivered by data scientists of the IMF Big Data Center through lectures and hands-on workshops covering various Big Data applications pertinent to macroeconomic statistics.
Offers insights from both producer and user standpoints.
Participants present and discuss their use cases, engage in discussions, and benefit from shared insights and experiences.

Participants should be able to:

Provide support in setting up Big Data architecture, encompassing data extraction, preprocessing, and visualization in their various organizations.
Demonstrate a practical knowledge of machine learning modeling, variable selection, performance analysis as well as model selection, for GDP nowcasting and trade monitoring (based on the IMF's 'PortWatch' platform).
Demonstrate practical skills in the use of Google Earth Engine (GEE), Dynamic World, Jupyter Notebook and other geospatial and data science packages to analyze satellite data and generate high-frequency macroeconomic statistics.
Carry out textual analysis with natural language processing (NLP) technologies to support macroeconomic analysis.
Demonstrate the application of these Big Data technologies and resources to improve timelines and granularity of their official statistics.
Facilitate peer-learning on Big Data applications and explore collaborations between agencies working on projects of mutual interest.
Gain insights into setting up effective data science teams and developing institutional Big Data strategies to support innovation for macroeconomic statistics.

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