AI-Based Economic Scenario Modelling and Monetary Policy Stress Testing Training Workshop

Course overview

This advanced workshop equips central bank economists, monetary policy analysts, financial stability specialists and macroeconomic researchers with practical methods for modelling alternative economic futures, assessing macro-financial vulnerabilities and evaluating the potential effects of monetary policy decisions. Participants explore how artificial intelligence, econometric forecasting, machine learning and scenario-based stress testing can complement established macroeconomic models. The programme focuses on central bank applications, including inflation forecasting, interest-rate decisions, exchange-rate shocks, liquidity conditions, credit growth, capital flows, fiscal pressures and financial stability.

The workshop emphasises AI-assisted analysis supported by economic theory, transparent assumptions, model validation and expert judgement. AI tools complement rather than replace structural economic models, policy judgement or institutional approval processes.

Course objectives

• Develop macroeconomic scenarios using economic theory, historical data, econometric models and AI-assisted analytical techniques.
• Translate model results into policy intelligence through sensitivity analysis, model validation, uncertainty assessment and clear briefings for monetary policy committees and financial stability authorities.
• Conduct macroeconomic and macro-financial stress tests to assess severe-but-plausible shocks, financial-sector vulnerabilities, central bank balance-sheet exposures and economic resilience.
• Apply AI and machine learning to scenario analysis for forecasting, shock detection, nonlinear relationships, early-warning indicators and alternative economic pathways.
• Evaluate monetary policy transmission by modelling how policy-rate changes and external shocks affect inflation, output, exchange rates, credit and financial conditions.

Who should attend

Course content

• Module 1: Foundations of Economic Scenario Modelling in Central Banks The role of scenario modelling in monetary policy formulation, inflation forecasting and financial stability; distinguishing baseline forecasts, alternative scenarios, stress scenarios
• Module 2: Macroeconomic Data Engineering and AI-Ready Data Preparing time-series data for economic modelling; working with inflation, GDP, policy rates, exchange rates, money supply, credit, employment, commodity prices, fiscal indicators and external-se
• Module 3: Advanced Econometric Models for Monetary Policy Application of VAR, Bayesian VAR, structural VAR, ARDL, error-correction models, local projections and state-space models; stationarity and cointegration; impulse response functions; forecast erro
• Module 4: DSGE and Semi-Structural Macroeconomic Modelling Foundations of dynamic stochastic general equilibrium models and semi-structural forecasting models; modelling households, firms, inflation expectations, output gaps, monetary policy rules and ex
• Module 5: AI and Machine Learning for Macroeconomic Forecasting Using random forests, gradient boosting, regularised regression and neural networks for economic forecasting; feature engineering, lag selection and nonlinear relationships; evaluating model
• Module 6: AI-Assisted Scenario Design and Shock Generation Transforming economic risks into coherent quantitative scenarios; using AI to support the identification and organisation of risk narratives; generating alternative assumptions for commodity pric
• Module 7: Inflation Dynamics and Monetary Policy Transmission Modelling demand-pull and cost-push inflation, exchange-rate pass-through, imported inflation, wage and price dynamics, inflation expectations and second-round effects; estimating policy-rate
• Module 8: Forecasting Inflation, GDP and Key Policy Variables Developing baseline forecasts and alternative projections for headline and core inflation, real GDP, output gaps, exchange rates, interest rates, credit and external balances; forecast combina
• Module 9: Monetary Policy Reaction Functions and Policy Simulations Estimating and interpreting monetary policy reaction functions, including Taylor-type rules; simulating alternative policy-rate paths under changing inflation and output conditions; asse
• Module 10: External Shocks and Open-Economy Scenario Modelling Modelling oil and food price shocks, global interest-rate changes, exchange-rate depreciation, trade disruptions, geopolitical events, capital-flow reversals and changes in international dema
• Module 11: Macroeconomic Stress Testing and Financial Stability Designing severe-but-plausible macroeconomic stress scenarios; assessing the effects of recession, inflation surges, interest-rate shocks, exchange-rate movements and sovereign risk; transla
• Module 12: AI-Enhanced Early-Warning Systems and Economic Risk Detection Using machine learning and statistical indicators to identify emerging inflation pressures, credit booms, external imbalances, market volatility and systemic vulnerabilities; develo
• Module 13: Integrated Macroeconomic and Financial Sector Scenarios Building connected scenarios across the real economy, monetary conditions, fiscal developments and financial institutions; examining the sovereign-bank nexus, public debt pressures, gover
• Module 14: Model Validation, Uncertainty and AI Governance Backtesting, out-of-sample evaluation, sensitivity analysis, parameter uncertainty, model comparison and robustness testing; explaining model limitations; monitoring drift in economic relationshi
• Module 15: Integrated Central Bank Scenario Modelling Capstone Participants develop a complete scenario analysis based on a realistic central bank policy challenge. They define the economic shock, prepare model inputs, generate baseline and adverse proje

General notes

• Practical Exercises and Case Studies AI-assisted inflation forecasting and forecast comparison, modelling an unexpected policy-rate increase and its transmission to output and inflation, assessing imported inflation following currency depreciation, developing baseline and adverse GDP scenarios, evaluating an international oil and food price shock, simulating capital outflows and foreign-exchange pressures, estimating the effects of global interest-rate increases on domestic financial conditions, conducting a sovereign debt and banking-sector stress scenario, forecasting credit growth and non-performing loans under recession conditions, building an early-warning indicator for inflation or financial instability, comparing machine-learning forecasts with econometric benchmarks, testing alternative monetary policy reaction paths, evaluating scenario sensitivity to different assumptions, validating AI-assisted scenario narratives against economic evidence, and presenting policy recommendations to a simulated monetary policy committee.
• Recommended Tools and Technology Stack Economic modelling and statistical analysis: EViews, Stata, R, Python, MATLAB or Dynare, depending on institutional licensing and existing workflows. Python and AI libraries: pandas, NumPy, statsmodels, scikit-learn, XGBoost, PyMC and suitable time-series or deep-learning libraries. Data visualisation and reporting: Microsoft Excel, Power BI, Jupyter notebooks and approved institutional reporting platforms. Modelling methods: VAR/SVAR, Bayesian VAR, ARIMA/SARIMA, local projections, state-space models, DSGE and semi-structural models, machine-learning forecasting, scenario analysis, Monte Carlo simulation, sensitivity testing and stress testing. Governance and quality assurance: Model documentation, data lineage, reproducible workflows, version control, model validation checklists and controlled AI environments. Tool selection can be adapted to the central bank’s current technology stack. Participants do not need to be expert programmers to benefit from the policy-oriented sessions, although hands-on modelling sessions will be most valuable for participants with experience in economics, statistics or data analysis.
• Practical Outputs and Deliverables A central bank economic scenario design framework. A structured macroeconomic dataset and data-quality checklist. A baseline and alternative inflation forecasting model. A monetary policy transmission analysis template. An AI-assisted scenario generation and validation framework. An external-shock and exchange-rate sensitivity model. A macro-financial stress-testing scenario matrix. A financial stability early-warning indicator framework. A model performance and validation scorecard. A scenario dashboard covering inflation, GDP, interest rates, exchange rates and credit conditions. A policy-rate simulation and scenario comparison report. A monetary policy committee briefing with key findings, uncertainties and policy implications.
• Deliverables:Central bank economic scenario design framework, structured macroeconomic dataset and data-quality checklist, baseline and alternative inflation forecasting model, monetary policy transmission analysis template, AI-assisted scenario generation and validation framework, external-shock and exchange-rate sensitivity model, macro-financial stress-testing scenario matrix, financial stability early-warning indicator framework, model performance and validation scorecard, scenario dashboard covering inflation, GDP, interest rates, exchange rates and credit conditions, policy-rate simulation and scenario comparison report, monetary policy committee briefing with key findings, uncertainties and policy implications

Frequently asked questions

Schedule & fees