A spreadsheet full of economic indicators means very little until someone applies the right statistical tools to it – and that gap between raw data and a defensible economic conclusion is exactly what this course closes. It equips participants with the in-depth knowledge and practical skills needed to apply econometric analysis techniques to real economic datasets.

Participants learn to handle complex economic data, understand the standard models used in econometric analysis, and implement these methods with advanced analytical software, building toward the ability to construct their own analytical models and make informed decisions from precise data.

Who Should Attend?

• Economists and economic analysts working with economic data in public or private institutions.
• Academics and researchers working on applications of econometric analysis.
• Students and graduates in economics and statistics looking to sharpen their analytical skills.
• Anyone interested in using economic data to make strategic decisions.

Course Objectives:

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

• Use econometric tools to analyse economic data.
• Apply mathematical and statistical models to reach accurate results.
• Implement econometric analysis using appropriate software.
• Interpret economic results and turn them into practical conclusions.
• Apply standard econometric tools to analyse economic variables across different contexts.

Module 1: Foundations of Econometric Analysis

• Defining econometric analysis and the difference between quantitative and qualitative economic analysis.
• Basic principles in economic analysis and the statistical foundations behind them, including independent versus dependent variables.
• Simple and multiple linear regression basics.

Module 2: Working with Economic Data in Analytical Software

• Software used in economic analysis, such as EViews and SPSS.
• Inputting and cleaning economic data.
• Building simple economic models using statistical programs.

Module 3: Advanced Regression and Model Validation

• Multiple regression, non-linear variables, and dummy variables in economic models.
• Hypothesis testing, checking for a normal data distribution, and validating models with F-tests and t-tests.

Module 4: Diagnosing and Fixing Model Problems

• Identifying and addressing multicollinearity.
• Handling heteroscedasticity (non-homogeneous error variance).
• Advanced techniques for improving model accuracy.

Module 5: Time Series and Error Correction Models

• Time series data, stationarity testing, and time-series regression.
• Autoregressive (AR), Moving Average (MA), and ARIMA models, plus cross-sectional data with time integration.
• Error Correction Models (ECM): short-term versus long-term models and interpreting their results.

Module 6: Forecasting and Financial Market Applications

• Structural models for analysing relationships between economic variables, and economic forecasting techniques.
• Assessing the accuracy of economic forecasts.
• Quantitative analysis of financial markets, including Value at Risk (VaR) models and using regression to measure economic risk.