This course covers advanced subjects in econometrics, focusing on time series and panel data. 

The following modules will be covered in this course:

1. Time Series Methods
   • Stationarity and Autocorrelation
   • ARIMA models
   • VAR
   • GARCH
2. Panel Methods
   • Fixed Effexts
   • Diff-in-diff
3. Further Topics in Econometrics (if time allows)

Prior knowledge of the following topics is expected:

• Multivariate regression (application, interpretation)
• Regression properties (least squares estimation, classical assumptions, estimator properties, Gauss-Markov theorem)
• Regression inference (hypothesis testing, confidence intervals, model selection)
• Assumption failures and remedies (heteroskedasticity, multicollinearity)
• Functional forms (dummy variables, interaction terms, log-transformations)
• Endogeneity (omitted variables, simultaneity, data errors)

These are covered in Econometrics I and Econometrics II – it is assumed that you have a solid understanding of them. In addition, it would be beneficial to have working knowledge of R, e.g. from the Statistics with R course or your experience in Econometrics I and Econometrics II.

After this course, you

• will know about and be able to use time series and panel data methods.
• will be equipped to independently conduct advanced econometric analyses.
• should be able to replicate empirical studies published in scientific journals.
• use econometric methods to pursue your own research questions independently.

Attendance is compulsory. Students are allowed to miss up to two units. Absences in the project presentation units should be avoided.

The course consists of

• Lectures with focus on econometric theory,
• examples of applications during the lectures,
• and a project where you independently apply what we learned in the course, either alone or in groups.

Assessments are based on four components. 

• 40% Exam
• 20% Project presentation
• 30% Project Report submission
• 10% Active participation

The grading scheme is

1. [87.5, 100]
2. [75, 87.5)
3. [62.5, 75)
4. [50, 62.5)
5. [0, 50)

Sound knowledge of basic statistics, mathematics, matrix algebra, OLS estimation and causal identification strategies is expected. Successful completion of Econometrics I and Econometrics II, as well as solid knowledge of R or similar software, is highly recommended.

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

sannah.tijani@wu.ac.at, maximilian.heinze@wu.ac.at