Big Data for Macroeconomic Statistics

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Big Data for Macroeconomic Statistics (BDMS)

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Session No.: JV 25.33

Location: Vienna, Austria

Date: October 27-31, 2025 (1 week)

Delivery Method: In-person Training

Primary Language: English

Target Audience

This course is specifically designed for technical staff within national statistics offices (NSOs) and central banks including economists, statisticians, and data scientists working on macroeconomic statistics, nowcasting, and data science projects.

Qualifications

Participants are expected to have a degree in economics, statistics, data science or equivalent experience.

Course Description

This workshop, presented by the Statistics Department of the IMF, is aimed at country officials from national statistics offices (NSOs) and central banks who are seeking to acquire the knowledge and skills necessary to leverage Big Data and advanced technologies for generating high-frequency and detailed statistics to enhance macroeconomic monitoring and research. IMF experts will deliver lectures and hands-on workshops covering various Big Data applications pertinent to macroeconomic statistics, offering insights from both producer and user standpoints. Furthermore, participants will have the opportunity to present and discuss their use cases, engage in discussions, and benefit from shared insights and experiences.

The workshop is conducted by staff of the IMF Big Data Center.

Course Objectives

Upon completion of this course, participants should be able to:

¿ Build Scalable Big Data Systems: Provide support in setting up Big Data architecture, encompassing data extraction, preprocessing, and visualization in their various organizations.
¿ Applying Machine Learning to Economic Forecasting: Develop skills in machine learning modeling, variable selection, performance analysis as well as model selection, for GDP nowcasting and trade monitoring using the IMF¿s PortWatch platform.
¿ Utilize Geospatial Analytics: 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.
¿ Implement NLP for Sentiment Analysis: Carry out sentiment analysis with natural language processing (NLP) technologies to support macroeconomic analysis.
¿ Enhance Official Statistics with Big Data: Demonstrate the application of these Big Data technologies and resources to improve timelines and granularity of their official statistics.
¿ Collaboration and Knowledge Sharing: Facilitate peer-learning on Big Data applications and explore collaborations between agencies working on projects of mutual interest.

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