An introductory course to econometrics and applied linear regression analysis.
Topics include, but are not limited to, least-squares regression, analysis of variance, model diagnostics, dichotomous, polytomous, longitudinal, and missing data.
The course will enable students to analyse data using regression models, diagnose their regression models, and interpret and report the regression results. Students will learn how to use the statistical computing software R that is widely-used in academia and industry (see, e.g. Bhalla, Deepanshu, 2016. “Companies using R.” ListenData).
The European (economy-priced) version does not include exercises, the appendices on fundamental math, probability, and statistics; yet it should be sufficient:
Wooldridge, J. M., 2014. Introductory Econometrics: EMEA Edition, 1st ed., Cengage Learning.
Available from amazon and bookdepository.
The required readings from this textbook are listed below.
- Fox, John, 2015. Applied regression analysis and generalized linear models, 3rd ed., Sage Publications.
Available from bookdepository and amazon.
Relevant chapters are listed below as [AR].
- Wickham, H. and Grolemund, G., 2016. R for data science, O’Reilly.
How to import, tidy, transform, and visualize data with R.
- Harrell, F., 2015. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer.
Advanced insights from an experienced practioner.
Students must read the corresponding chapters of the textbook before each session. Students need access to a computer with the statistical computing software R. The software is available free of charge from www.r-project.org. As a graphical frontend RStudio is recommended.
Class meets each Monday at 12:15 till 14:45.
Prof. Dr. Dennis A. V. Dittrich
You can always reach me via email. Appointments for meetings can be arranged through my webpage at: http://economicscience.net/content/book-appointment.
Updated information, links to the literature, additional materials, etc. can be found on my webpage as well.
A typical 3 credit course requires 150 hours of your time. The table below identifies how I expect those 150 hours will be allocated. While you do not receive direct marks for reading, reading will affect your class participation (your ability to participate in class discussions and activities) and your final exam mark.
|Class Time (3 hours / week)||45 hours|
|Reading (3 hours / week)||45 hours|
|Problem Sets (2 hour / week)||30 hours|
|Preparation and Review (2 hours / week)||30 hours|
Topics and Reading Assignments
Session 1: 12.2.
- Introduction to course and to regression (Ch. 1)
Session 2: 19.2.
The simple regression model (Ch. 2)
Session 3: 26.2.
- Multiple regression analysis: Estimation (Ch. 3)
Session 4: 05.03.
- Multiple regression analysis: Inference (Ch. 4)
Session 5: 12.03.
- Multiple regression analysis: Asymptotics (Ch. 5)
Session 6: 19.03.
- Multiple regression analysis: Further Issues (Ch. 6)
[AR:4, 5, 7, 22]
Session 7: 26.03.
- Multiple regression analysis with Qualitative Information (Ch. 7)
Session 8: 16.04.
- Heteroskedasticity (Ch. 8)
Session 9: 23.04.
- Specification and Data Issues (Ch. 9)
[AR:6, 11, 13, 19, 20]
Session 10: 30.04.
- Simple Panel Data Methods (Ch. 13)
Session 11: 07.05.
- Advanced Panel Data Methods (Ch. 14)
Session 12: 14.05.
- Instrumental Variables Estimation (Ch. 15)
Session 13: 28.05.
- Limited Dependent Variable Models (Ch. 17)
[AR: 14, 15]
Session 14: Tuesday 29.05., 15:30-18:00
- Carrying Out an Empirical Project (Ch. 19)
Session 15: 04.06.
Final exam (oral)
Topics and reading assignments are subject to changes.