Big Data for Macroeconomic Statistics (BDMS)
Source details
- Canonical URL
- Big Data for Macroeconomic Statistics (BDMS)
Other formats
Bibliographic details
- Session: JV 25.33
- Location: Vienna, Austria
- Dates: October 27-31, 2025 (1 week)
- Delivery method: In-person Training
- Primary language: English
- Status: Deadline passed
Key logistics
- Session No.: JV 25.33
- Location: Vienna, Austria
- Date: October 27-31, 2025 (1 week)
- Delivery Method: In-person Training
- Primary Language: English
- Status: Deadline passed
Target audience
- 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.
Course description
- The workshop is presented by the Statistics Department of the IMF and conducted by staff of the IMF Big Data Center.
- Aimed at country officials from national statistics offices (NSOs) and central banks who seek knowledge and skills to leverage Big Data and advanced technologies for generating high-frequency and detailed statistics to enhance macroeconomic monitoring and research.
- IMF experts deliver lectures and hands-on workshops covering various Big Data applications pertinent to macroeconomic statistics, offering insights from both producer and user standpoints.
- Participants present and discuss their use cases, engage in discussions, and benefit from shared insights and experiences.
Course objectives
- ¿ 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.
References