MATLAB Econometrics Toolbox for Macroeconomic Forecasting Training Course

Course overview

Macroeconomic forecasting teams need more than spreadsheet extrapolation when policy shifts, revisions, structural breaks, and interdependent indicators affect the outlook. Economists, central-bank analysts, treasury teams, and strategy functions must produce forecasts that can be reproduced, stress-tested, and explained to decision-makers. This course addresses the practical challenge of building defensible inflation, GDP, interest-rate, exchange-rate, and labour-market forecasts in MATLAB using documented data pipelines, econometric models, and forecast-evaluation procedures.

Participants work directly with MATLAB and Econometrics Toolbox to prepare time-series data, diagnose stationarity and cointegration, estimate ARIMA and VAR models, generate conditional and unconditional forecasts, and compare model performance. The course covers state-space modelling, Kalman filtering, forecast intervals, scenario analysis, rolling-window backtesting, and forecast combination. Participants also learn to automate repeatable workflows with MATLAB scripts and live scripts, turning raw economic releases into decision-ready charts, tables, and model outputs.

Instructor-led demonstrations are followed by guided coding labs using a realistic macroeconomic dataset. Each participant develops a reusable forecasting workflow for a selected macroeconomic series or country dataset: data import and transformation code, model-estimation scripts, diagnostics, forecast charts, accuracy metrics, and an assumptions log. The final workshop focuses on presenting an evidence-based baseline forecast and alternative scenarios to a simulated economic or finance committee.

The programme suits professionals who already understand core econometric concepts and want to apply them rigorously in MATLAB. It is especially valuable where forecasting must be auditable, regularly updated, and credible under challenge from senior management, risk functions, or external stakeholders.

Course objectives

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

• Build a reproducible MATLAB workflow to import, align, transform, and document macroeconomic time-series data.
• Diagnose stationarity, autocorrelation, seasonality, and structural breaks using Econometrics Toolbox tests and visual diagnostics.
• Estimate and interpret ARIMA and ARIMAX models for inflation, output, interest-rate, or exchange-rate forecasting.
• Specify vector autoregression models and produce impulse-response functions, forecast-error variance decompositions, and multi-series forecasts.
• Test for cointegration and estimate error-correction models for long-run macroeconomic relationships.
• Implement state-space models and Kalman filtering to handle missing observations, revisions, and latent macroeconomic components.
• Evaluate competing forecasts with rolling-origin backtests, RMSE, MAE, bias measures, and prediction-interval coverage.
• Deliver a MATLAB-based macroeconomic forecast pack containing code, model diagnostics, scenarios, forecast charts, and an assumptions log.

Benefits of attending

Target competencies

Who should attend

• Macroeconomists — who need reproducible models for inflation, growth, labour-market, and policy-rate projections
• Economic Analysts — who convert official releases and market data into recurring forecast publications
• Central Bank Analysts — who assess transmission mechanisms, policy scenarios, and uncertainty around macroeconomic outlooks
• Treasury and ALM Analysts — who require interest-rate, exchange-rate, and inflation forecasts for balance-sheet planning
• Financial Planning and Analysis Managers — who need macroeconomic assumptions that can be traced to tested models
• Quantitative Risk Analysts — who translate macroeconomic scenarios into stressed risk and capital assessments

Requirements and prerequisites

Participants should be comfortable writing and running basic MATLAB scripts, working with vectors, matrices, tables, timetables, and plots. They should understand introductory regression, hypothesis testing, time-series notation, and the meaning of lags, residuals, confidence intervals, and forecast errors. Prior exposure to ARIMA or VAR models is useful but not essential; the course develops these methods from practical model specification onward. Participants need access to MATLAB with Econometrics Toolbox installed on a current laptop. Prior programming in Python, R, or C++, advanced calculus, machine learning experience, or prior use of MATLAB live scripts is not required.

Training methodology

The five days combine concise instructor-led modelling sessions with daily MATLAB coding labs. Participants import and transform a macroeconomic dataset, estimate models in Econometrics Toolbox, interpret diagnostic output, and compare forecasts under changing assumptions. Case exercises use realistic issues such as data revisions, missing observations, inflation shocks, and policy-rate scenarios. Small-group reviews focus on model-selection decisions and forecast communication. On the final day, participants assemble and present a forecast pack, then create an application plan identifying the data, model candidates, governance checks, and reporting cycle for their own workplace.

Course outline

Day 1: Macroeconomic Data and MATLAB Forecasting Foundations

• MATLAB project structure, scripts, live scripts, and reproducible forecasting folders
• Importing CSV, Excel, and database extracts into tables and timetables
• Datetime alignment, frequency conversion, and calendar-aware macroeconomic series handling
• Log, difference, annualised growth, and seasonal-adjustment transformations
• Missing-value treatment, outlier inspection, and economic-data revision tracking
• Exploratory time-series plots, autocorrelation functions, and partial autocorrelation functions
• Train-test splits and forecast-horizon design for macroeconomic applications

Workshop: Build a MATLAB data-preparation script that converts raw inflation, GDP, unemployment, and policy-rate releases into an aligned forecasting timetable.

Day 2: Univariate Models for Core Macroeconomic Series

• Stationarity assessment with augmented Dickey-Fuller and KPSS tests
• Lag selection using AIC, BIC, and residual diagnostic evidence
• AR, MA, ARMA, and ARIMA model specification in Econometrics Toolbox
• Seasonal ARIMA modelling for monthly and quarterly indicators
• ARIMAX models with exogenous drivers and intervention variables
• Residual autocorrelation, normality, heteroskedasticity, and structural-break diagnostics
• Point forecasts, forecast intervals, and dynamic versus static forecasting

Workshop: Estimate competing ARIMA and ARIMAX models for consumer-price inflation and produce a 12-month forecast with diagnostic charts and intervals.

Day 3: Multivariate Dynamics and Long-Run Relationships

• Vector autoregression specification for interdependent macroeconomic variables
• VAR lag-order selection and stability-condition testing
• Granger causality tests and interpretation limits in economic forecasting
• Impulse-response functions for policy, demand, and supply shocks
• Forecast-error variance decomposition for driver analysis
• Johansen cointegration testing for nonstationary macroeconomic series
• Vector error-correction models and long-run equilibrium adjustment

Workshop: Construct a VAR or VECM for output growth, inflation, and the policy rate, then create an impulse-response briefing chart for a rate shock.

Day 4: State-Space Models, Scenarios, and Forecast Evaluation

• State-space representations of trend, cycle, and seasonal components
• Kalman filtering and smoothing for incomplete or revised data
• Latent-variable estimation and nowcasting concepts
• Conditional forecasts based on assumed paths for policy rates or exchange rates
• Scenario design for baseline, upside, downside, and shock cases
• Rolling-origin backtesting and expanding-window forecast evaluation
• RMSE, MAE, forecast bias, Diebold-Mariano tests, and interval coverage

Workshop: Use a state-space model to estimate a latent trend in GDP growth and compare its out-of-sample accuracy with an ARIMA benchmark.

Day 5: Forecast Production, Governance, and Decision Communication

• Forecast combination using weighted, equal-weight, and benchmark approaches
• Automating monthly or quarterly forecast reruns with MATLAB functions
• Parameter storage, data lineage, and model assumptions documentation
• Model-governance checks for validation, challenger models, and approval review
• Publication-ready forecast charts, fan charts, and scenario comparison tables
• Communicating uncertainty, revisions, and model limitations to non-technical audiences
• Forecast-pack design for economic committees, treasury reviews, and management planning

Workshop: Complete and present a MATLAB macroeconomic forecast pack containing reproducible code, model comparison results, baseline and downside scenarios, and an executive briefing chart.

Tools & standards covered

MATLAB, Econometrics Toolbox, Statistics and Machine Learning Toolbox, Database Toolbox

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