Syllabus

Title
5415 Econometrics
Instructors
PD Florian Huber, Ph.D.
Contact details
florian.huber@wu.ac.at
Type
PI
Weekly hours
4
Language of instruction
Englisch
Registration
02/19/18 to 02/25/18
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Econometrics
Dates
Day Date Time Room
Wednesday 03/07/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 03/14/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 03/21/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 04/11/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 04/18/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 04/25/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 05/02/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 05/09/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 05/16/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 05/23/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 05/30/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 06/06/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 06/13/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 06/20/18 08:30 AM - 11:30 AM D4.0.144
Wednesday 06/27/18 08:30 AM - 11:30 AM D4.0.144

On this page:

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Contents

The course provides an introduction to Bayesian econometrics. The first part of the course ismainly concerned with introducing the students to the Bayesian paradigm applied to simplemodels. Specifically, the first lectures deal with the Bayesian analysis of the linear regressionmodel under conjugate- and non-conjugate priors. Moreover, a brief introduction to limiteddependent variable models is provided. The second half of the course deals with the Bayesiananalysis of uni- and multivariate time series models like standard autoregressive models andvector autoregressions. 

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Learning outcomes

This course introduces the students to basic concepts in Bayesian econometrics. In addition, a large share of lectures is devoted to advanced topics in time series analysis.  Upon completion of this course, students should be able to understand empirical studies published in scientific journals as well as carry out advanced econometric work by themselves. This, to some extent, also includes being able to design their own estimation code.


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Teaching/learning method(s)
lectures and exercises
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Assessment

i) Exercises: 20%

ii) A brief term paper: 40%

iii) Final exam: 40%

A positive final test (50% threshold of total points) is required for passing the course.

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Prerequisites for participation and waiting lists


 

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Readings
1 Author: Koop, G
Title:

Bayesian Econometrics

Publisher: Wiley
Remarks: It is expected that the students read the corresponding chapters before each lecture. Selected papers that will be distributed in class.
Year: 2003
Content relevant for class examination: Yes
Recommendation: Essential reading for all students
Type: Book
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Recommended previous knowledge and skills

Students should have a sound knowledge of statistics and mathematics (matrix algebra in particular).

 

 

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Availability of lecturer(s)

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Other

Supplementary Literature

Lancaster, T. (2004). Introduction to Modern Bayesian Econometrics, Wiley-Blackwell.

Kruschke, J.K. (2010). Doing Bayesian Data Analysis: A Tutorial with R and Bugs.  

Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis, NewYork: Springer.


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Unit details
Unit Date Contents
1
Last edited: 2017-10-23



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