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COURSE OUTLINE

Applied Econometrics

1. General

School

School of Finance and Statistics

Academic Unit

Department of Banking and Financial Management

Level of Studies

Undergraduate

Course code

ΧΡΕΦΟ01

Semester

5th or 7th

Course Title

Applied Econometrics

Idependent Teaching Activities

Weekly Teaching Hours

Credits

Lectures
4
7,5

Course Type

Special background

Prerequite Courses

Language of Instruction and Examinations

Greek

Is the course offered to Erasmus Students?

Yes

Url (Eclass)

https://eclass.unipi.gr/courses/XTDUG105/

2. Learning Outcomes

Learning Outcomes

Upon successful completion of the course, students should be able to:

  • Understand how an econometric model is appropriately specified and estimated for the purpose of forecasting the economic variables under study, supporting optimal decision-making, and/or informing the conduct of economic policy.
  • Understand how applied econometric analysis can be combined with economic theory in order to enhance the explanatory power of different economic/financial models.
  • Use econometric methods to assess whether specific economic theories and hypotheses are supported by the empirical evidence, with the aim of modifying theoretical economic/financial models so that they are consistent with observed economic data.
  • Understand the main steps involved in the estimation of a linear regression model, taking into account relationships among macroeconomic, financial, and other relevant variables.
  • Understand the structure and estimation of Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregressive (VAR) models.
  • Understand the methodological steps required to generate forecasts of the dependent variable of interest, such as GDP or stock market returns.
  • Integrate the above concepts and techniques in order to conduct a comprehensive applied econometric analysis using the R programming language.
General Competences
  • Search for, analysis and synthesis of data and information, with the use of the necessary technology
  • Decision-making
  • Working independently

3. Syllabus

  • The linear regression model: introduction to multivariate regression analysis. In particular, we demonstrate the form of a multivariate linear regression model, how we interpret the estimated parameter coefficients of the model, its underlying basic assumptions, alongside with the estimation of the model parameters via the least-squares method.
  • Collection and analysis of data for the development of an econometric model.
  • Hypothesis testing within the regression analysis framework: t tests, F-tests.
  • Multicollinearity: methods to identify whether colinearity exists among the explanatory variables of a model and how we deal with this problem.
  • Testing for autocorrelation and how we deal with this problem.
  • Testing for the presence of heteroskedasticity in the residuals of a regression model and how we deal with this problem.
  • Methods for variable selection: choosing the best subset of explanatory variables to ensure that the regression model has enhanced predictive power.
  • Structural stability of the model parameter coefficients: identifying whether the parameter coefficients of a regression model are stable and methods to deal with the problem of parameter instability.
  • Estimation of Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregressive (VAR) models.
  • Panel Data Models: Specification and estimation of econometric models using data that vary across multiple dimensions, particularly time and cross-sectional units, such as firms, countries, or other economic entities.

4. Teaching and Learning Methods - Evaluation

Delivery

Face-to-face

Use of Information and Communications Technology

  • Use of PowerPoint presentations during lectures.
  • Solving exercises and case studies both on the board and through the use of statistical and econometric software.
  • Implementation of econometric analysis using the R programming language.
  • Use of electronic communication, particularly email, to communicate with students, respond to questions and queries, and facilitate the submission of assignments.
  • Use of the eclass electronic learning platform to provide lecture material and additional supporting resources, including academic articles, case studies, programming code, and other relevant material.

Teaching Methods

Activity

Semester Workload

Lectures
30
Laboratory practice
135,5
Study and analysis of bibliography
22
Course Total
187,5

Student Performance Evaluation

  • Midterm Project – 30% of the final grade
  • Written Final Examination – 70% of the final grade

5. Attached Bibliography

Suggested Bibliography
  • Instructor’s lecture notes (PowerPoint slides) and exercises available through eclass.unipi.gr.
  • Jack Johnston, John Dinardo. Econometric Methods, Kleidarithmos, 4th edition, 2005.
  • Dimitrios Astriou, Stephen Hall. Applied Econometrics, Propompos, 3rd edition, 2018.
  • Delis, P., Degiannakis, S., and Filis, G. (2022). “What matters when developing oil price volatility forecasting frameworks?” Journal of Forecasting, 41(2), 361–382.
  • Greene, Willian H. (2012). Econometric Analysis. Prentice Hall, New York.
  • Graham, E., and Timmermann, A. (2008). “Economic Forecasting.” Journal of Economic Literature, 46(1), 3–56.
  • Hamilton, J. (1994). Time Series Analysis. Princeton University Press, New Jersey.
  • Studenmund, A. H. (2017). Using Econometrics: A Practical Guide. Pearson.
  • Christou, G. K. (2002). Introduction to Econometrics. Gutenberg.
Related Academic Journals