316202 Statistical and Econometric Models (V) (SoSe 2017)

Contents, comment

The objective of this course is to get the participants up and running with some
basic statistical/econometric techniques, in particular, estimation and inference
with linear and generalized linear models for cross-sectional and panel data.

Doing (applied) statistics/econometrics means using a computer full time and---to
some extend---this class is going to reflect this fact. Students will have the opportunity
to pick up some skills in using the statistical software R.

Requirements for participation, required level

Students are assumed to have basic knowledge of multivariate calculus, basic statistics
and probability, as well as matrix algebra.

A prior course on real analysis/measure-theoretic probability is certainly helpful but not required.
The same applies to further statistical/econometric preknowledge.

Some experience with the statistical software R (or some comparable package) is handy, but
can be picked up during the class as well.

Bibliography

Main text: Verbeek, A Guide to Modern Econometrics

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  

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Subject assignments

Module Course Requirements  
31-M-Ectr1 Econometrics 1 Statistical and Econometric Models Graded examination
Student information

The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.

Degree programme/academic programme Validity Variant Subdivision Status Semester LP  
Studieren ab 50    

Each week there will be some mandatory reading and a list of exercises.
Students are expected to do the reading, attend the lectures, attempt some
of the exercises on their own, and actively participate in the tutorial sessions.

Grades will be assinged on the basis of a midterm and a final exam.

E-Learning Space
E-Learning Space
Registered number: 69
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Address:
SS2017_316202@ekvv.uni-bielefeld.de
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If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_89022485@ekvv.uni-bielefeld.de
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12 Students to be reached directly via email
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Last update basic details/teaching staff:
Thursday, February 2, 2017 
Last update times:
Wednesday, November 8, 2017 
Last update rooms:
Wednesday, November 8, 2017 
Type(s) / SWS (hours per week per semester)
lecture (V) / 4
Language
This lecture is taught in english
Department
Faculty of Business Administration and Economics
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89022485