Syllabus

Title
1842 Econometrics II
Instructors
Univ.Prof. Mag.Dr. Harald Oberhofer
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/12/24 to 09/17/24
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Monday 10/07/24 02:00 PM - 04:00 PM TC.3.12
Monday 10/14/24 02:00 PM - 04:00 PM TC.3.12
Monday 10/21/24 02:00 PM - 04:00 PM TC.3.12
Monday 10/28/24 02:00 PM - 04:00 PM TC.3.12
Monday 11/04/24 02:00 PM - 04:00 PM TC.3.12
Monday 11/11/24 02:00 PM - 04:00 PM TC.3.12
Monday 11/18/24 02:00 PM - 04:00 PM TC.3.12
Monday 11/25/24 02:00 PM - 04:00 PM TC.3.12
Monday 12/02/24 02:00 PM - 04:00 PM TC.3.12
Monday 12/09/24 02:00 PM - 04:00 PM TC.3.12
Monday 12/16/24 02:00 PM - 04:00 PM TC.3.12
Monday 01/13/25 02:00 PM - 04:00 PM TC.3.12
Contents

The econometrics teaching program is offered in a cycle over 3 terms. In Econometrics I, the foundations of the subject are dealt with: causality, correlation, assumptions of the linear regression model, OLS estimation, asymptotic tests, misspecification,outliers, heteroskedasticity and an introduction to E-views. In Econometrics II, advanced subjects are covered: Time series analysis, endogeneity, instrumental variable estimation, limited dependent variable models, simultaneous equation models and panel data models. In Applied Econometrics, a deeper analysis of selected topics is offered and students are required to write an empirical, applied-econometric essay. 

Learning outcomes

This course provides an introduction to the analysis of economic datausing econometric methods. After having taken the course, students should beable to understand empirical studies published in scientific journals and carryout econometric work by themselves. The course complements and expands thesubjects dealt with in Econometrics I. 

Attendance requirements

Attendance is compulsory. A maximum of 2 lessons may be missed.

Teaching/learning method(s)
The course consists of lectures where the theoretical frameworks are presented and practical units where students assess the methods using real data.
Assessment

i) Exercises + class participation: 30% ii) Midterm exam: 35% iii) Final exam: 35%

A positive combined midterm and final exam (50% threshold) is a prerequisite for passing the course. 

Prerequisites for participation and waiting lists

sound knowledge of basic statistics, mathematics, and matrix algebra

Readings

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Recommended previous knowledge and skills

sound knowledge of basic statistics, mathematics, and matrix algebra

Availability of lecturer(s)

harald.oberhofer@wu.ac.at

Last edited: 2024-04-25



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