Syllabus

Title
1840 Bayesian Econometrics I
Instructors
Univ.Prof.i.R. Dr. Sylvia Frühwirth-Schnatter
Contact details
sylvia.fruehwirth-schnatter@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/19/17 to 11/05/17
Registration via LPIS
Notes to the course
Subject(s) Doctoral/PhD Programs
Advanced Course Research Methods of Social and Economic Sciences
Research Methods
Methodology and Theory
Dates
Day Date Time Room
Wednesday 11/08/17 05:00 PM - 07:30 PM D4.4.008
Wednesday 11/15/17 05:00 PM - 07:30 PM D4.4.008
Wednesday 11/29/17 05:00 PM - 07:30 PM D4.4.008
Wednesday 12/06/17 05:00 PM - 07:30 PM D4.4.008
Wednesday 12/13/17 05:00 PM - 07:30 PM D4.4.008
Wednesday 12/20/17 05:00 PM - 07:30 PM D4.4.008
Wednesday 01/10/18 05:00 PM - 07:30 PM D4.4.008
Wednesday 01/17/18 05:00 PM - 07:30 PM D4.4.008
Wednesday 01/24/18 05:00 PM - 07:30 PM D4.4.008

On this page:

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Contents

The course provides an introduction to Bayesian econometrics.  Topics covered by course are

I. Fundamentals of Bayesian econometrics:
  • Bayesian inference 
  • Simulation techniques

II. Applications:

  • Linear regression and extensions
  • Time series
  • Endogeneity and sample selection
  • Multivariate responses and panel data
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Learning outcomes
After completing this course the student will have the ability to:
  • Recall the basic principle of Bayesian econometrics
  • Apply public domain packages for Bayesian econometrics and to analyse and evaluate the output of such packages
  • Design and implement computer programs for solving computational problems in Bayesian inference for commonly applied econometrics models
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Teaching/learning method(s)
This course is taught as lectures and tutorials combined with course projects which have to be solved individually by the students. In combination with the lectures, the course projects will help students to consolidate and expand their understanding of the theoretical and applied methods discussed in the lectures. For the course project, students create a report or a presentation of an R-based solution to a given computational task in Bayesian econometrics.
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Assessment

Assessment is based on five home assignments where students solve case studies as well as a project presentation of applied research involving Bayesian methods at the end of the term. Each home assignment accounts for 10% of the grade, whereas the project presentation accounts for 50% of the grade.

The assessment of the home assigment is based on the correctness of results. Assessment of the presentation is based the clarity of the presentation, the ability to describe and apply the key concepts discussed throughout the course, and the recognizable effort made.

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Readings
1 Author: Edward Greenberg
Title: Introduction to Bayesian Econometrics

Publisher: Cambridge University Press
Edition: 1. Auflage
Year: 2008
Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book
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Recommended previous knowledge and skills

Basic knowledge in probability theory and statistics

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Availability of lecturer(s)
sylvia.fruehwirth-schnatter@wu.ac.at
Last edited: 2017-11-13



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