About This Course
In an era of increasing economic volatility and data abundance, the ability to apply rigorous quantitative methods to macroeconomic and financial questions has become a critical professional competency. Economists, central bank analysts, financial researchers, and policy advisers are increasingly expected to move beyond theoretical frameworks and produce empirically grounded, model-based evidence that can withstand scrutiny and inform high-stakes decisions.
The Applied Econometrics for Macro-Economics and Finance Training programme at TrainingComplex is designed to close the gap between econometric theory and real-world application. Over ten intensive days, participants work through a structured curriculum that spans classical regression analysis, time-series modelling, panel data methods, volatility modelling, and macroeconomic forecasting — all anchored in hands-on practice with actual datasets drawn from central banks, national statistics offices, and financial markets across Africa and beyond.
The programme uses EViews, Stata, and R as its primary analytical environments, ensuring participants leave with transferable software skills alongside conceptual mastery. Each module pairs theoretical exposition with guided empirical exercises, so participants experience first-hand how modelling choices affect conclusions, how to diagnose and correct common econometric problems, and how to communicate results clearly to technical and non-technical audiences alike.
Whether you are a graduate economist entering a research role, a finance professional seeking to strengthen your quantitative toolkit, or a policy analyst responsible for macroeconomic surveillance, this course provides the structured, applied training needed to elevate your analytical output and professional credibility.
Who Should Attend
- Economists and economic analysts in government ministries and central banks
- Financial analysts and investment researchers in banks, asset management firms, and brokerage houses
- Policy researchers and advisers working on macroeconomic surveillance and fiscal analysis
- Statisticians and data analysts seeking to apply econometric methods to economic and financial datasets
- Academic researchers and postgraduate students conducting empirical economic studies
- Risk management professionals responsible for macroeconomic stress-testing and scenario analysis
- Monetary policy and financial stability teams in regulatory and supervisory authorities
- Consultants advising on economic modelling, forecasting, and impact evaluation
Learning Objectives
- Understand the theoretical foundations of classical and generalised least squares regression in an economic and financial context
- Estimate, interpret, and validate single-equation and multiple-equation regression models using real macroeconomic and financial data
- Apply unit root and cointegration tests to establish the long-run relationships between macroeconomic variables
- Build and interpret Vector Autoregression (VAR) and Vector Error Correction (VECM) models for macroeconomic analysis
- Model and forecast financial market volatility using ARCH, GARCH, and related conditional heteroscedasticity frameworks
- Conduct panel data analysis using fixed-effects, random-effects, and dynamic panel estimators appropriate to macro-financial datasets
- Apply structural break tests and regime-switching models to account for economic crises and policy shifts
- Develop and evaluate macroeconomic forecasting models, including factor models and mixed-frequency approaches
- Diagnose and remedy common econometric problems including heteroscedasticity, autocorrelation, multicollinearity, and endogeneity
- Interpret impulse response functions, forecast error variance decompositions, and other outputs of multivariate time-series models
- Construct event study and high-frequency financial econometrics frameworks for market microstructure analysis
- Communicate econometric findings effectively through written reports, tables, and visualisations suited to policy and investment audiences
- Use EViews, Stata, and R confidently to implement the full range of estimation and testing procedures covered in the programme
Course Modules
Module 1: Foundations of Econometric Analysis in Economics and Finance
- The role of econometrics in macroeconomic and financial research
- Review of probability, statistical inference, and sampling distributions
- The classical linear regression model: assumptions and properties
- Software orientation: navigating EViews, Stata, and R for econometric work
- Distinguishing causation from correlation: identification challenges in economics and finance
- Overview of the econometric modelling cycle from data collection to policy-relevant inference
Module 2: Ordinary Least Squares Estimation and Inference
- OLS estimation mechanics and the Gauss-Markov theorem
- Hypothesis testing: t-tests, F-tests, and confidence intervals in economic models
- Goodness-of-fit measures and their limitations in financial applications
- Interpreting regression coefficients in macroeconomic and financial contexts
- Partitioned regression and the Frisch-Waugh-Lovell theorem: isolating partial effects
- Practical issues in OLS implementation: scaling, units of measurement, and numerical precision
Module 3: Specification, Functional Form, and Qualitative Variables
- Model specification strategies and the consequences of misspecification
- Logarithmic, polynomial, and interaction term specifications in economic models
- Incorporating dummy variables for policy events, regimes, and seasonal patterns
- Lagged variables and distributed lag models in macroeconomic analysis
- RESET and other specification tests for detecting functional form inadequacy
- Controlling for structural heterogeneity using slope interaction terms across groups or periods
Module 4: Diagnosing and Correcting Classical Regression Violations
- Testing for and addressing heteroscedasticity: White and Breusch-Pagan tests, WLS, and robust standard errors
- Autocorrelation: Durbin-Watson, Breusch-Godfrey tests, and Cochrane-Orcutt correction
- Multicollinearity: detection, consequences, and remediation strategies
- Endogeneity and the method of instrumental variables and two-stage least squares
- Weak instrument diagnostics: the Cragg-Donald statistic and Stock-Yogo critical values
- Model evaluation after correction: verifying that remediation restores classical assumptions without introducing new distortions
Module 5: Introduction to Time-Series Analysis
- Properties of time-series data: trend, seasonality, cyclicality, and stationarity
- Autocorrelation and partial autocorrelation functions: identification and interpretation
- ARIMA modelling: Box-Jenkins methodology applied to macroeconomic indicators
- Seasonal adjustment and the X-13 approach for official economic statistics
- Forecast generation and evaluation within the ARIMA framework using out-of-sample testing
- Spectral analysis and frequency-domain methods for isolating cyclical components in macroeconomic data
Module 6: Unit Roots, Cointegration, and Long-Run Relationships
- Unit root tests: ADF, PP, and KPSS tests and their application to financial and macroeconomic series
- Spurious regression and the importance of stationarity in financial modelling
- Engle-Granger and Johansen cointegration frameworks
- Error Correction Models: capturing long-run equilibrium and short-run dynamics
- Determining cointegration rank: trace and maximum eigenvalue statistics in the Johansen procedure
- Weak exogeneity and identification of adjustment coefficients within the VECM representation
Module 7: Vector Autoregression and Multivariate Time-Series Models
- VAR model specification, lag selection criteria, and stability conditions
- Impulse response functions and forecast error variance decompositions
- Granger causality testing in macroeconomic and monetary policy contexts
- Structural VAR identification strategies: Cholesky decomposition and sign restrictions
- Bayesian VAR methods: Minnesota priors and shrinkage estimation for large systems
- Forecast performance evaluation of VAR models against univariate and judgmental benchmarks
Module 8: Panel Data Econometrics for Macro-Financial Analysis
- Structure and advantages of panel data in cross-country macroeconomic research
- Fixed-effects and random-effects estimators: theory, application, and the Hausman test
- Dynamic panel models and the Arellano-Bond GMM estimator
- Panel unit root and panel cointegration tests for cross-country financial datasets
- Handling unbalanced panels and missing data in international macroeconomic datasets
- Cross-sectional dependence: Pesaran CD test and common correlated effects estimation
Module 9: Volatility Modelling and Financial Econometrics
- Stylised facts of financial returns: fat tails, volatility clustering, and leverage effects
- ARCH and GARCH models: specification, estimation, and interpretation
- Extensions: EGARCH, GJR-GARCH, and multivariate DCC-GARCH frameworks
- Applications to Value at Risk, portfolio risk management, and asset pricing
- Backtesting volatility models: Kupiec's proportion-of-failures and Christoffersen's interval forecast tests
- Realized volatility measures constructed from high-frequency intraday data and their integration with GARCH-family models
Module 10: Structural Breaks, Regime Switching, and Non-Linear Models
- Testing for structural breaks: Chow, CUSUM, and Bai-Perron multiple breakpoint tests
- Markov-switching models for business cycle and financial regime identification
- Threshold autoregressive models in macroeconomic and interest rate analysis
- Handling crises and policy discontinuities in empirical macroeconomic models
- Smooth transition autoregressive models: STAR and LSTAR specifications for gradual regime change
- Model comparison across linear and non-linear specifications using information criteria and out-of-sample forecast accuracy
Module 11: Macroeconomic Forecasting Methods
- Evaluation of forecast accuracy: RMSE, MAE, Theil's U, and Diebold-Mariano tests
- Factor models and principal component analysis for large macroeconomic datasets
- Mixed-frequency models: MIDAS and nowcasting for high-frequency economic surveillance
- Combining forecasts and constructing prediction intervals for policy use
- Fan chart construction and communicating forecast uncertainty to policy and investment audiences
- Real-time data vintages and the role of data revisions in assessing genuine out-of-sample forecast performance
Module 12: Communicating Econometric Results for Policy and Investment Audiences
- Structuring econometric research reports for central bank and government audiences
- Visualising time-series, regression, and volatility results for maximum clarity
- Translating statistical significance into economic and financial significance
- Responding to technical peer review and presenting findings to non-specialist decision-makers
- Crafting executive summaries that distil model uncertainty and scenario assumptions for senior stakeholders
- Ethical obligations in empirical reporting: disclosure of model selection, data limitations, and sensitivity to key assumptions
Module 13: Capstone Project: Full Econometric Modelling Workflow
- Research question formulation, data sourcing, and preliminary exploratory analysis
- Model selection, estimation, and iterative diagnostic testing
- Forecasting, scenario simulation, and sensitivity analysis
- Presentation of findings and peer critique: replicating the professional research review process
- Documenting the modelling workflow to ensure transparency, reproducibility, and auditability
- Incorporating referee feedback and revising the empirical strategy to produce a polished, policy-ready output
Requirements & Prerequisites
Participants should hold a degree in economics, finance, statistics, or a related quantitative discipline and have a working knowledge of basic statistical concepts such as probability distributions, hypothesis testing, and simple regression. Familiarity with at least one statistical software package — such as Excel, Stata, EViews, or R — is recommended, as the course moves quickly into applied work. Prior exposure to introductory econometrics at undergraduate level is beneficial but not strictly required, as the programme begins with a consolidating review of core methods.
Training Methodology
- Structured lecture sessions that pair econometric theory with the economic and financial intuition behind each technique
- Guided hands-on lab sessions in EViews, Stata, and R using curated macroeconomic and financial datasets from African and global sources
- Case study analysis of published central bank, IMF, and World Bank empirical studies, examining modelling choices and their implications
- Group modelling projects in which teams specify, estimate, diagnose, and present a complete econometric model on an assigned macroeconomic or financial research question
- Diagnostic workshops focused on identifying and correcting real econometric problems embedded in pre-prepared flawed datasets
- Interpretive exercises requiring participants to translate regression output, impulse response charts, and volatility plots into plain-language policy or investment narratives
- Peer critique sessions in which teams review and provide structured feedback on each other's model specifications and results
- Daily consolidation reviews that connect each new technique to methods already covered, reinforcing the cumulative logic of the curriculum
- A capstone individual assignment in which each participant applies the full modelling workflow — specification, estimation, testing, forecasting, and reporting — to a dataset relevant to their own organisation
Organisational Benefits
- Enhanced capacity to produce rigorous, model-based economic analysis that supports evidence-informed policy and investment decisions
- A team equipped to conduct independent macroeconomic and financial research without reliance on external consultants
- Improved quality and credibility of economic forecasts, fiscal projections, and financial risk assessments
- Stronger institutional capability for macroeconomic surveillance, monetary policy analysis, and financial stability reporting
- Reduced analytical errors arising from misapplied methods or undiagnosed econometric problems
- Practical software proficiency in EViews, Stata, and R that can be immediately deployed on live projects
- Better-prepared staff for engagement with international financial institutions, donor partners, and technical peer review processes
- A culture of quantitative rigour that strengthens the organisation's analytical reputation and attracts high-calibre talent
Where Is Applied Econometrics for Macro-Economics and Finance Training Offered?
This course runs in-person across: Cape Town, Lagos, Mombasa, Dubai, Nakuru, Riyadh, Nairobi, Zanzibar, Kigali, Kampala, Dar es Salaam, Abuja, Naivasha, Johannesburg, Accra, Arusha, Kisumu, Addis Ababa, Pretoria, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Cape Town, South Africa |
In-Person |
28 Sept 2026 – 9 Oct 2026 |
USD 7,600 |
| Lagos, Nigeria |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 5,000 |
| Mombasa, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Dubai, United Arab Emirates |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 8,200 |
| Nakuru, Kenya |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
KES 230,000 |
| Live Virtual |
Online |
17 Oct 2026 – 6 Dec 2026 |
KES 160,000 |
| Riyadh, Saudi Arabia |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 9,000 |
| Cape Town, South Africa |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 7,600 |
| Nairobi, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 230,000 |
| Zanzibar, Tanzania |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 5,000 |
| Kigali, Rwanda |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 3,800 |
| Kampala, Uganda |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 3,800 |
| Dar es Salaam, Tanzania |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 4,600 |
| Abuja, Nigeria |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 5,600 |
| Naivasha, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
+233 more dates — view live schedule →
Frequently Asked Questions
Do I need prior econometrics training to attend this course?
A basic grounding in statistics and at least introductory economics or finance is expected. The programme begins with a consolidating review of regression fundamentals, so participants with undergraduate-level exposure to econometrics will be well placed, even if their knowledge is rusty. Those with no prior quantitative background may find the pace challenging.
Which software will be used, and do I need to install it in advance?
The course uses EViews, Stata, and R. Participants should bring a laptop with R and RStudio installed (both are free); licensed copies of EViews and Stata will be available in the training environment. Installation guides are sent to confirmed participants two weeks before the programme begins.
Will I receive a certificate on completion?
Yes. Participants who attend at least 80% of sessions and complete the capstone assignment receive a TrainingComplex Certificate of Completion in Applied Econometrics for Macro-Economics and Finance, which can be referenced on professional profiles and CVs.
Are the datasets used in the course relevant to African economies?
Yes. While the curriculum draws on internationally recognised datasets such as IMF World Economic Outlook, World Bank WDI, and financial market data, a significant proportion of the hands-on exercises use data from African central banks, national statistics offices, and regional financial markets, making the applications directly relevant to participants working in the region.
Can organisations send multiple staff members at a group rate?
Yes. TrainingComplex offers group enrolment discounts for three or more delegates from the same organisation. In-house delivery of this programme can also be arranged for teams of eight or more, with datasets and case studies tailored to the organisation's specific sector and analytical needs. Contact our client services team for a customised proposal.
