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

• Apply foundational time-series and econometric methods for revenue forecasting.
• Develop and utilize different types of economic models (e.g., buoyancy, elasticity) for tax analysis.
• Forecast the revenue impact of proposed tax policy changes, incorporating behavioral responses.
• Differentiate between static and dynamic revenue scoring models and apply them appropriately.
• Integrate macroeconomic variables and forecasts into the revenue projection process.
• Analyze the sensitivity of revenue forecasts to changes in underlying economic assumptions.
• Critically evaluate and select appropriate data sources for tax modeling and forecasting.
• Develop clear, structured reports to communicate complex revenue forecasts to policymakers.

• Economists and Analysts in Ministries of Finance/Treasury
• Staff of Parliamentary Budget Offices and Fiscal Councils
• Central Bank Research Economists
• Revenue Forecasting and Budgeting Specialists
• Academics and Consultants specializing in Fiscal Policy
• Senior Tax Policy Officers

• Hands-on computer lab sessions using statistical software (e.g., R, Stata, Eviews) for model building.
• Group project: developing a full-scale revenue forecast for a specific tax category.
• Policy simulation exercise: estimating the impact of a hypothetical tax reform (dynamic scoring).
• Peer review of forecasting methodology and assumptions.
• Discussions on the political pressures and ethical concerns in revenue forecasting.

Unit 1: Principles of Revenue Forecasting

• The role of revenue forecasting in the budget cycle and fiscal planning
• Statistical vs. economic approaches to forecasting
• Identifying and managing inherent uncertainties in revenue projections
• Key data requirements for accurate tax forecasting

• Definition and calculation of tax elasticity (discretionary policy changes)
• Definition and calculation of tax buoyancy (automatic changes due to economy)
• Estimating elasticity for major tax types (Income, VAT, Excises)
• Modeling the impact of tax base changes and structural shifts

Unit 2: Revenue Modeling Methodologies

• Application of regression analysis (OLS, ARIMA) to revenue data
• Incorporating macroeconomic variables (GDP, inflation, trade) into models
• Techniques for short-term vs. long-term revenue projections
• Dealing with data limitations and structural breaks in time series

• The concept of static scoring and its limitations
• Principles of dynamic scoring and modeling behavioral responses
• The role of micro-simulation models in detailed policy analysis
• Modeling general equilibrium effects of major tax reforms

Unit 3: Forecasting Specific Tax Types

• Modeling individual income tax based on employment and wage growth
• Forecasting corporate income tax and the role of profit volatility
• Incorporating tax expenditure estimates into the forecast

• Modeling VAT/Sales Tax based on private consumption and imports
• Forecasting excise duties and their dependence on consumption patterns
• Analyzing the impact of trade policy on customs and duties revenue

Unit 4: Model Validation and Uncertainty Management

• Backcasting, ex-post evaluation, and measures of forecast error
• Benchmarking performance against internal and external forecasts
• Techniques for model maintenance and updating

• Developing best-case, worst-case, and baseline scenarios
• Performing sensitivity analysis on key economic drivers
• Presenting forecast ranges and associated risks to decision-makers

Unit 5: Institutional and Policy Alignment

• The importance of independent, transparent forecasting institutions
• Best practices for coordination between revenue, budget, and central bank functions
• Communicating forecast results clearly and managing expectations