Overview: The course provides an introduction to Bayesian econometrics. It starts by reviewing the basic ideas and tools of Bayesian inference and proceeds to the Bayesian analysis of the linear regression model and its extensions. Different posterior simulation methods such as Monte Carlo integration and Gibbs sampling (Markov Chain Monte Carlo (MCMC)) employed in modern Bayesian econometrics are introduced. Several macroeconomic applications with focus on forecasting and model selection are considered.
Prerequisites: Basic knowledge of econometrics and time series analysis (for instance, Advanced Econometrics or the course in the analysis of stationary time series offered by the Department of Mathematics and Statistics, or equivalent) is assumed. In class, R software package will be used to demonstrate the methods. However, any available computer program may be used for the assignments and the term paper.
Textbooks: The lectures will be based on the relevant parts of the following:
Koop, G. (2003). Bayesian Econometrics, Wiley.
Lancaster, T. (2004). An Introduction to Modern Bayesian Econometrics, Blackwell.
Lectures: On Tuesdays and Wednesdays at 12-14 (from 1 September to 7 October), seminar room 2. Lecture slides (based mainly on Koop (2003)) as well as links to supplementary material such as programs and data used in class will be posted here. Skimming through the slides before the lecture is highly advisable, and you should have the printouts available in class.
Exercises (Teacher: Doctoral Student Yin Ming): The exercise sessions will take place in the seminar room 2 on Mondays 14.9, 28.9. and 5.10 at 10-12 and on Friday 9.10. at 12-14. There will be four homework assignments during the course (each consisting of a number of exercises). The exercises will involve empirical analyses and analytical problems. For PhD students, additional challenging exercises are provided. For credit, the solutions of the assignment should be returned by 6 p.m. (by e-mail) on the work day preceding each exercise session. In addition, you must be prepared to present your solution at the exercise session. Each assignment is graded on the scale from 0 to 5. The grading criteria are as follows: 0 = less than 50% of the exercises done, 1 = at least 50% of the exercises done showing good effort, 2 = at least 70% of the exercises done showing good effort, 3 = at least 80% of the exercises done showing good effort, 4 = at least 90% of the exercises done showing good effort, and 5 = at least 90% of the exercises done and the solutions are very good. The assignments and the data sets will be posted here.
Term Paper: You must write an empirical research report based on at least one of the methods presented in the course (PhD students will employ simulation methods). 2/5 of the grade will be based on the term paper. The term paper should be returned by e-mail by Monday 3 November, 2014. The detailed instructions will be made available in due course.
Grading: The final grade of the course is computed as the weighted average of the grades of the final exam (2/5), the term paper (2/5), and the homework assignments (1/5). All four parts are separately graded on a scale from 0 to 5. In order to pass the course you must get at least 1 in each.