Registration via LPIS
|Wednesday||10/05/22||04:00 PM - 06:00 PM||D4.0.022|
|Wednesday||10/12/22||04:00 PM - 06:00 PM||D4.0.022|
|Wednesday||10/19/22||04:00 PM - 06:00 PM||D4.0.022|
|Wednesday||11/02/22||04:00 PM - 05:30 PM||D5.1.001|
|Wednesday||11/09/22||04:00 PM - 06:15 PM||D5.1.001|
|Wednesday||11/16/22||04:00 PM - 06:15 PM||D4.0.022|
|Wednesday||11/23/22||04:00 PM - 06:15 PM||D5.1.001|
|Wednesday||11/30/22||04:00 PM - 06:00 PM||D5.1.001|
|Wednesday||01/11/23||03:30 PM - 06:00 PM||D5.1.001|
|Wednesday||01/18/23||03:30 PM - 06:30 PM||D4.0.022|
The course provides an introduction to Bayesian econometrics. Topics covered by course areI. Fundamentals of Bayesian econometrics:
- Bayesian inference
- Simulation techniques
- Linear regression and extensions
- Time series
- Endogeneity and sample selection
- Multivariate responses and panel data
- 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
Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.
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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