AI-Enhanced Econometrics & Applied Statistics

Online Workshop June 2026

Contact for Onsite/Private/Corporate Training

Deadline: 15th May

Standard Fees: €950

Group Fees (3+) / GEM/CGE program graduate / Affiliates: 680€

Student Fees (valid ID required): 410€

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Workshop Description:

AI-Driven Econometric Modeling

Participants gain practical skills in combining traditional econometric techniques with modern machine-learning tools for stronger prediction, inference, and analytical accuracy.

A compact and intensive 10-hour live, online training designed to equip participants with modern skills at the intersection of econometrics, machine learning, and applied data science. This workshop blends theory with hands-on practice, enabling participants to apply AI-enhanced tools for forecasting, causal inference, policy analysis, financial modeling, and climate-related analytics. Participants will learn how to

Integrate AI and machine-learning techniques with traditional econometric models to improve prediction and inference.
Clean, preprocess, and engineer features from real-world economic and financial datasets.
Apply supervised and unsupervised ML methods(e.g., random forests, gradient boosting, clustering) for empirical economic analysis.
Estimate causal effects using modern tools such as Causal ML, synthetic control, and treatment-effect algorithms.
Build advanced time-series forecasting models including hybrid econometric–ML systems, LSTM networks, and Prophet-based forecasts.
Run scenario analysis and generate automated reports for policy evaluation, financial risk assessment, and climate-related projections.
Create interactive dashboards and visualizations for communicating complex analytical results effectively.
Implement hands-on case studies using real data from economics, public policy, finance, and climate analytics.

Advanced Forecasting

Hands-on training in ML-based forecasting (LSTM, Prophet) and causal methods (Causal ML, synthetic control, treatment effects) applied to real economic and policy data.

Applied Data Science and Visualization

Learners build dashboards, automate reporting, and perform big-data exploration using Python and modern visualization tools to support real-world decision-making.

Workshop Highlights

5-Day online weekend (Saturdays and Sundays) workshop, totaling 15 hours.

Practical simulation tutorials using Python, R and Power BI

Live interactions with a moderator will be
facilitated, offering participants the opportunity to ask questions and engage in extended discussions.

Participants work directly with real datasets from economics, finance, policy, and climate studies, gaining skills they can apply immediately in their own work.

Develop forecasting models using neural networks (LSTM/GRU), hybrid ML–econometric approaches, and scenario-based forecasting.

Meet The

Moderators

J. Riveros Economic advisor in Colombian government, Senior Trainer at M&S Research Hub Institute and postgraduate of Munich Graduate School of Economics.

G. Santangelo is a Senior Lecturer in the Department of Economics as well as the Director of the International Political Economy Program at Fordham University and a term professor at the Stillman School of Business at Seton Hall University. He earned his Ph.D. and Master of Arts (M.A.) in Economics from Fordham University, as well as a Bachelor of Science (B.S.) in Economics from Seton Hall University.

Target Participants

Economists, data analysts, researchers, policy specialists.
Financial analysts, graduate students, and professionalsworking in government agencies, central banks, consulting firms
Anyone seeking practical skills in AI-enhanced econometrics, machine learning, forecasting, and applied statistical analysis