Understand the theoretical basis of econometric modelling and the assumptions underlying classical regression analysis. Estimate linear regression models using Ordinary Least Squares (OLS) and assess the validity of their results. Apply diagnostic tests to detect and correct violations of classical assumptions (heteroscedasticity, autocorrelation, multicollinearity). Extend basic regression analysis to include qualitative variables, limited dependent variables, and simultaneous equations. Analyse time-series data, test for unit roots, and model cointegrating relationships. Use econometric software to conduct empirical analyses on real economic data. Critically evaluate published empirical studies and assess the soundness of their econometric methodology.

Econometrics is the application of statistical and mathematical methods to the analysis of economic data. This course provides students with the theoretical foundations and practical tools necessary to specify, estimate, test, and interpret econometric models. Emphasis is placed on the classical linear regression framework, the properties of estimators under various conditions, and extensions to handle real-world data challenges. Students will gain hands-on experience applying econometric techniques using statistical software, critically evaluating empirical research, and conducting their own data analysis.

Lecture-based teaching

Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. Stock, J. H. & Watson, M. W. (2019). Introduction to Econometrics (4th ed.). Pearson.