Course Description

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.

Course Objectives

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).

Course Materials


The course will follow the textbook:
Wooldridge, J. M., 2016. Introductory Econometrics: A Modern Approach, 6th ed., Cengage Learning.
Available from amazon and bookdepository.

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.

Further recommendations:

  • 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.

  • The Principles of Econometrics with R, available online, is an applied introduction to both R and econometrics and thus highly recommended for this course.

    A brief introduction to the software R and RStudio can be found online here and a slightly longer version online here.

Course Requirements:

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 As a graphical frontend RStudio is recommended.

Class meets each Monday at 12:15 till 14:45.

Instructor Information:

Prof. Dr. Dennis A. V. Dittrich

You can always reach me via email. Appointments for meetings can be arranged through my webpage at:

Updated information, links to the literature, additional materials, etc. can be found on my webpage as well.

Grading Guidelines:

Grading ComponentWeight
Problem Sets35%
Final Examination65%


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)

    Please review the material in appendix A: Basic Mathematical Tools and in appendix D: Summary of Matrix Algebra.

Session 2: 19.2.

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: Further Issues (Ch. 6)
    [AR:4, 5, 7, 22]

Session 8: 16.04.

  • Multiple regression analysis with Qualitative Information (Ch. 7)
    [AR:7, 8]

Session 9: 23.04.

  • Heteroskedasticity (Ch. 8)
    [AR: 12]

Session 10: 30.04.

  • Specification and Data Issues (Ch. 9)
    [AR:6, 11, 13, 19, 20]

Session 11: 07.05.

  • Simple Panel Data Methods (Ch. 13)

Session 12: 14.05.

  • Advanced Panel Data Methods (Ch. 14)

Session 13: 28.05.

  • Instrumental Variables Estimation (Ch. 15)

  • Limited Dependent Variable Models (Ch. 17)
    [AR: 14, 15]

  • Review

Session 14: 04.06.

  • Carrying Out an Empirical Project (Ch. 19)

  • Review

    Final exam (oral)

Session 15: 11.06.

Final exam (oral)

Topics and reading assignments are subject to changes.

Click here for the Dropbox folder with additional material and homework problem sets.