Learning Outcomes
Upon successful completion of the course, students will be able to: 1. Apply classical and advanced econometric methodologies. 2. Interpret the results of econometric analysis conducted using computer software. 3. Interpret and evaluate research findings reported in academic journals and books.
Course Content (Syllabus)
Indicative module content: The simple linear model, the multivariate linear model, diagnostic tests and inference, tests of the appropriateness of the functional form, the use of dummy variables, the concept of stationarity, unit root tests, unit root tests with structural changes, Granger causality concept and tests, vector autoregression models (VAR), linear cointegration tests, error correction models (ECM, VECM), logit and probit models, traditional panel models (pooled, fixed effects, random effects)
Additional bibliography for study
Asteriou, D., & Hall, S.G. (2021). Applied econometrics. Bloomsbury Publishing.
Hanck, C., Arnold, M., Gerber, A., Schmelzer, M. (2024). Introduction to econometrics with R. 2024-02-13. University of Duisburg-Essen.
Heiss, F. (2020). Using R for introductory econometrics. CreateSpace Independent Publishing Platform.
Kleiber, C., & Zeileis, A. (2008). Applied econometrics with R. Springer Science & Business Media.
Hill, R.C., Griffiths, W.H., Lim, G.C. (2018) Principles of econometrics. 5th edn. John Wiley and Sons.
Stock, H.J. and Watson, M.W. (2020) Introduction to econometrics. 4th global edn. Pearson.