data handling (filtering, compilation, weighting, filtering bias, summaries...)

model development (data transformations, formulating estimation equations, identification...)

interpreting results (elasticities, semi-elasticities, interaction terms...)

brush-up of basic econometrics along the way (multivariate least squares regression,
IV and 2SLS, panel data estimation, limited dependent variables)

The goal of this course is to convey a solid understanding of applied econometric work. Students will learn to compile data, formulate estimation models, make sense of the results of various tests and interpret results. At the end of the semester, students will understand the pros and cons of various common modelling approaches and estimation techniques and how to deploy the latter in empirical work.

Lecturing and exams will take place in classroom until further notice. So classroom attendance is obligatory. Unujustified missing more that two classes twice worsens grading. For a missed (or unsuccessful) exam we have an extra date (12th unit).

Should C19 restrictions become effective, we will switch to distance learning completely (with streamed lectures and online exams).

Lecturing, assignments (home work), exams.  Course subject is mainly chapters 4-7 of Marno Verbeek's textbook, with some deepening based on other textbooks. Lectures will focus on implementation of already mastered econometric concepts in programming language R. Also assignments will focus on applied aspects. Exams, instead, will focus on correct interpretation of output of relevant statistical programs.

Assignments (homework):  30 points (6 x 5 points)

Exams: 70 points (30 + 40 points)

Exams in units 6 and 11 (planned: Nov 13 and Dec 18).

Substitute date for one of the above exams (chosen freely): unit 12 (planned: Jan 8)

Grading: At least 50, 62.5, 75, 87.5 points for grades 4, 3, 2, 1. Negative grading (5) below 50 points.

Basic acquaintance with (statistical/mathematical programming language) R and the associated scripting environment R-Studio (both open source and free for personal use)

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.

Verbeek, chapters 1-3  and appendices A and B (or equivalent):

basic statistics, regression modelling, least squares (LS) estimation,
interpretation of LS estimators,  testing of restrictions,
finite sample vs. asymptotic properties of LS estimators

individual appointment