Last update:2014/03/04

The course schedule can be either found in the KUSSS-system or in the syllabus.

Objectives

The Course (CS) and the Intensifying Course (IC) Econometrics I are part of the Econometrics Curriculum of the Department of Economics. Its goal is to provide students an understanding of simple econometric models and to introduce them to deal with empirical work in a critical manner. The main target audience is therefore students of the Bachelor program in Business & Economics who aim to specialize in either Economics or in Management and Applied Economics. Of course, students of other areas of specialization, or other programs who are interested in empirical methods are highly welcome. The CS's focus is on econometric theory and applications are being taught in the accompanying IC. All IC meetings will take place in the computer lab.

Topics

• Recap of Multiple Regression Analysis (MRA)
• MRA: Further Issues (scaling, functional form, residual analysis, etc.)
• MRA: Binary Variables (on the lhs and the rhs)
• Heteroskedasticity
• More on Specification Data Issues (tests, proxy variables, measurement error, etc.)
• Basics with Time Series Data

Prerequisites

In order to register, students need basic knowledge in mathematics, statistics and simple econometrics. See, for instance, Appendix A to C and chapters 1 to 4 in Wooldridge (200x). For the IC you should be also familiar with the statistical software pack Stata. The two prerequisite courses CS & IC Empirische Wirtschaftsforschung are offered every winter term (link).

Readings

The primary textbooks for these courses are:

• CS: Wooldridge, Jeffrey M. (200x), Introductory Econometrics: A Modern Approach, 3rd ed. or higher, South Western College Publishing, Mason (Ohio).
• IC: (optional): Adkins, Lee C und R. Carter Hill (2008), Using Stata for Principles of Econometrics, 3rd ed., Wiley.

Additional reading material is listed in the reading list (reading list). Required readings are indicated by *.

Material

• Recap of Multiple Regression Analysis (MRA): Slides
• Further Issues: Slides, Do-file, Problem Set I
• Binary Variables: Slides, Problem Set II
• Heteroskedasticity: Slides, Do-file, Problem Set III
• Specification Data Issues: Slides, Problem Set IV
• Basics with Time Series Data: Slides, Notes on TSD in Stata, Problem Set V

Meetings

CS: Before students attend the CS, they are asked to study the relevant chapters in the text book. In class the material is discussed together thoroughly. Two short tests on the reading assignments will be given during the semester (without extra notice!). On each test students can gain a maximum of 20 points. The better score will account for the final grade. At the end of the semester a final exam (with a maximum of 100 points) will be given. The grading scale for the CS will be as follows:

[Note this follows the Austrian system of scholastic grades. This is a 5-point grading scale, where ‘very good’ is the best possible grade, ‘good’ is the next-highest, ‘satisfactory’ indicates average performance, ‘adequate’ is the lowest passing grade and ‘unsatisfactory’ is the lowest and the only failing grade.]

Results: Summer Term 2014

IC: The integral parts of the IC are five take-home problem sets consisting of practical exercises. The integral parts of the IC are five take-home problem sets consisting of practical exercises. Each problem set consists of about five exercises. The problem sets can be downloaded from the courses' website. To solve the exercises, students are asked to use Stata. In class, the instructor will present the solutions of the exercises in the computer lab. This is followed by a general discussion of the motivations, intuitions, and analytics of each exercise. At the end of the semester a final exam (with a maximum of 100 points) will be given in the computer lab. The grading scale for the IC will be as follows: