Advanced Econometric Modelling and Forecasting Training Course

Course overview

Economic and financial decisions are often made using forecasts that hide unstable relationships, untested assumptions, and misleading measures of accuracy. Analysts need to distinguish correlation from defensible economic evidence, account for non-stationary time series, and explain why a model is suitable for a particular decision. This course equips participants to build, test, compare, and communicate advanced econometric models for forecasting inflation, demand, revenue, credit conditions, commodity prices, exchange rates, and other business-critical variables.

Participants work through the full modelling process: data preparation, exploratory analysis, stationarity testing, model specification, estimation, diagnostics, and forecast evaluation. They apply multiple regression, ARIMA and SARIMA models, vector autoregression (VAR), vector error-correction models (VECM), cointegration testing, volatility modelling with ARCH/GARCH, and panel-data methods. The course also covers lag selection, structural-break tests, multicollinearity, autocorrelation, heteroskedasticity, endogeneity, impulse-response analysis, scenario forecasting, and rolling-origin backtesting.

Instructor-led technical sessions are paired with guided modelling labs using realistic economic and financial datasets. Participants diagnose flawed models, compare alternative specifications, interpret output, and defend modelling choices to non-technical stakeholders. Each participant leaves with a documented forecasting workbook or script, including data definitions, diagnostic results, model-selection rationale, forecast accuracy measures, and a concise management-ready forecast briefing that can be adapted for workplace use.

The programme is designed for economists, financial analysts, forecasting specialists, risk professionals, and quantitative managers who already use data and need stronger econometric judgement. It is particularly valuable where forecasts influence budgeting, pricing, investment, treasury, risk appetite, or strategic planning.

Course objectives

By the end of this course, participants will be able to:

• Specify econometric models by linking economic theory, variable definitions, lag structures, and causal assumptions
• Test time-series data for stationarity using ADF, Phillips-Perron, and KPSS procedures
• Estimate and diagnose ARIMA and SARIMA forecasting models using residual analysis and information criteria
• Build VAR and VECM models to analyse dynamic relationships, cointegration, and impulse responses
• Model conditional volatility with ARCH/GARCH specifications for financial and risk-sensitive series
• Evaluate competing forecasts using rolling-origin backtests, MAE, RMSE, MAPE, and Diebold-Mariano tests
• Diagnose and address autocorrelation, heteroskedasticity, multicollinearity, structural breaks, and endogeneity
• Produce a documented forecast model and management briefing with assumptions, uncertainty ranges, and recommended actions

Benefits of attending

Target competencies

Who should attend

• Economists and Senior Economists — who produce macroeconomic, sector, or policy forecasts that require rigorous validation
• Financial Planning and Analysis Managers — who need defensible revenue, cost, cash-flow, and budget forecasts
• Quantitative Analysts — who build statistical models for markets, pricing, risk, or operational decisions
• Treasury and Market Risk Analysts — who assess interest-rate, exchange-rate, liquidity, and volatility exposures
• Credit Risk and Portfolio Analysts — who model default drivers, portfolio performance, and economic scenarios
• Data Analysts and Business Intelligence Specialists — who need to move from descriptive reporting to econometric forecasting

Requirements and prerequisites

Participants should be comfortable interpreting regression output and working with quantitative data in spreadsheets or statistical software. Prior knowledge of ordinary least squares, hypothesis testing, confidence intervals, p-values, basic matrix notation, and descriptive time-series concepts is assumed. Experience importing datasets, creating variables, and producing charts in at least one analytical tool is strongly recommended. Participants should bring a laptop capable of running the course software. Prior programming expertise is not required: guided code templates are provided for R and Python. This is not a first course in statistics or introductory regression.

Training methodology

The programme combines instructor-led econometric explanation with daily software labs using economic and financial datasets. Participants estimate models in R, Python, Stata, or EViews, inspect residuals and diagnostic plots, and compare forecasts against held-out observations. Short case discussions focus on decisions such as budget setting, exchange-rate exposure, and credit-risk scenarios. Group review sessions require participants to challenge model assumptions and interpret results for management. On the final day, each participant develops an application plan and completes a documented forecast briefing based on a realistic business case.

Course outline

Day 1: Specification, data quality and regression diagnostics

• Economic theory, causal diagrams, and model specification choices
• Data dictionaries, transformations, outliers, and missing-value treatment
• Logarithmic, growth-rate, index-number, and deflated variable construction
• Ordinary least squares assumptions and coefficient interpretation
• Multicollinearity assessment using correlation matrices and variance inflation factors
• Heteroskedasticity tests and heteroskedasticity-robust standard errors
• Autocorrelation detection using residual plots, Durbin-Watson, and Breusch-Godfrey tests

Workshop: Participants audit a revenue-driver regression dataset, estimate competing specifications, and produce a diagnostic memo identifying model weaknesses.

Day 2: Univariate time-series forecasting

• Trend, seasonality, cycles, and calendar effects in economic time series
• Stationarity concepts and spurious-regression risk
• ADF, Phillips-Perron, and KPSS unit-root tests
• Autocorrelation and partial-autocorrelation plots for order identification
• ARIMA and SARIMA model specification and estimation
• Information-criterion comparison using AIC, BIC, and HQIC
• Residual whiteness tests and forecast-interval construction

Workshop: Participants build and validate a seasonal ARIMA forecast for monthly demand or inflation, producing a forecast chart with prediction intervals.

Day 3: Multivariate dynamics and long-run relationships

• Distributed-lag models and lag-length selection
• Granger causality tests and their interpretation limits
• Vector autoregression model specification and stability conditions
• Impulse-response functions and forecast-error variance decomposition
• Johansen cointegration testing and cointegrating-rank selection
• Vector error-correction models for short-run adjustment and long-run equilibrium
• Structural-break tests, intervention variables, and regime-change handling

Workshop: Participants estimate a VAR or VECM for interest rates, inflation, and output, then prepare an impulse-response interpretation for a policy shock.

Day 4: Risk, volatility and panel-data models

• Return series, volatility clustering, and stylised financial facts
• ARCH effects testing using the Engle LM test
• GARCH, EGARCH, and GJR-GARCH specification choices
• Volatility forecasting and value-at-risk input interpretation
• Balanced and unbalanced panel-data structures
• Fixed-effects, random-effects, and Hausman specification testing
• Endogeneity, instrumental variables, and two-stage least squares

Workshop: Participants model exchange-rate or asset-return volatility with GARCH and estimate a panel model for firm or regional performance drivers.

Day 5: Forecast evaluation, scenarios and executive communication

• Training, validation, and test-set design for temporal data
• Rolling-origin and expanding-window forecast backtesting
• Accuracy metrics: MAE, RMSE, MAPE, sMAPE, and bias
• Diebold-Mariano tests for comparing forecast performance
• Benchmark models, forecast combinations, and model-risk controls
• Scenario design, stress assumptions, and fan-chart communication
• Reproducible reporting with code, model documentation, and decision briefings

Workshop: Participants complete an end-to-end forecasting case, delivering a tested model, accuracy comparison, scenario forecast, and management-ready recommendation.

Tools & standards covered

R, Python, Stata, EViews

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions