# AØKK08359U Summerschool 2020: Bayesian Econometrics (F) CANCELED

Volume 2019/2020
Education

MSc programme in Economics – elective course

The PhD Programme in Economics at the Department of Economics - elective course with research module. PhD students must contact the study administration and the lecturer in order to register for the research module.

Content

This course provides an introduction to modern Bayesian methods in econometrics.

The first part of the course presents the fundamentals of the Bayesian approach, from the derivation of Bayes' theorem to its practical application to econometric models. It introduces basic concepts such as prior, posterior and predictive distributions, before presenting essential tools based on simulation methods: Markov chain Monte Carlo methods, including the Gibbs sampler and the Metropolis-Hastings algorithm. Common econometric models students are already familiar with will be revisited from a Bayesian perspective (e.g., linear regression model, binary/discrete variable models).

The second part of the course dives into more specific and technical topics. It presents some selected econometric models where Bayesian methods are particularly useful, such as latent variable models and random coefficient models (relying on data augmentation methods). It also discusses some problems that can affect standard simulation methods (e.g., slow convergence, bad mixing), and explains how these problems can be successfully overcome using recent developments in statistics.

Bayesian methods can be applied to any field of economics. The examples and exercises offered during the summer school will be drawn from various topics, including micro- and macro-econometrics, and finance.

The main goal of this course is to provide students with practical skills to apply Bayesian methods to a specific problem. Therefore, it should be of particular interest to students planning on writing a Master's thesis or preparing for a PhD programme.

Learning Outcome

After completing the course the student is expected to be able to:

Knowledge:

• Account for Bayes' theorem and how it can be applied in econometrics.

• Account for the principles of simulation methods and how they can be used to make inference.

Skills:

• Select the most appropriate method for a given estimation problem.

• Master and implement Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hastings algorithm, both theoretically (analytical derivation of the algorithm) and practically (programming).

• Write code to implement Bayesian methods.

• Develop a computer program with the R programming language or use publicly available packages to carry out their own empirical analysis.

Competencies:

• Conduct a full Bayesian analysis:

• Formulate an economic model,

•  Organize prior knowledge and ”beliefs” about the model (prior),

•  Use relevant data to express the observed information in the model (likelihood),

• Use Bayes' theorem to update beliefs (posterior),

• Derive an appropriate algorithm to compute the posterior distribution,

• Write code to implement the algorithm,

• Interpret the results and criticize the model.

Lynch, Scott M. (2007). Introduction to Applied Bayesian Statistics and Estimation for

Social Scientists. Springer. ISBN 978-0-387-71264-2. (available as PDF from KU library)

Lancaster, Tony (2004). An Introduction to Modern Bayesian Econometrics. Blackwell

Publishing. ISBN 978-1-405-11720-3.

Recent research articles on selected topics will be introduced and studied during the course. They will be made available on the course website.

It is strongly recommended that a course in econometrics (Econometrics II or similar) has been followed prior to attending this summer school. Students should feel comfortable with basic elements of probability (marginal, conditional and joint distribution of random variables, law of large numbers, central limit theorem, likelihood principle, etc.) and with standard econometric methods (maximum likelihood estimation, method of moments, etc.).
Should these requirements not be completely fulfilled before the start of the summer school, a reading list will be provided before the start of the summer to prepare appropriately.

The R programming language will be introduced and used in this course. This programming language is not a prerequisite, but it is required that students have some programming experience. Students will be allowed to use a different language (like MATLAB), but examples and support will only be provided in R. Tutorials will be provided before the start of the summer school so that students get a good grasp of the basic features of R.

It is recommended to bring a laptop, but it is not a prerequisite.
The summer school will combine formal lectures with exercise classes and computer tutorials.

Since Bayesian approaches rely on simulation methods, the course will have an important computational component. Students will be trained to develop algorithms and to code them using the R programming language.

Students will be asked to prepare exercises and computer tutorials in groups. To this end, student groups (two to three students, depending on the total number of participants) will be formed at the beginning of the summer school.
Schedule:

The summer school has been canceled.

Teaching: Monday the 6th of July to Friday the 17th: 9am–12 noon and 1pm–3pm, except Wednesdays afternoons.

Lectures in the morning, exercises/computer tutorials in the afternoon (subject to
changes).

Timetable and venue: (Available from April 1st, 2020)
To see the time and location of classroom please press the link under "Se skema" (See schedule) at the right side of this page.

You can find the similar information in English at
https:/​/​skema.ku.dk/​ku1920/​uk/​module.htm
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-B5-5F20; [Name of course]””
-Select Report Type: "List - Week Days"
-Select Period: “Forår/Spring – Week 5-30”
Press: “ View Timetable”
• Category
• Hours
• Exam
• 20
• Lectures
• 30
• Practical exercises
• 16
• Preparation
• 140
• Total
• 206
Oral
Individual
Collective

The lecturer will give collective oral feedback on the exercises during the exercise classes, as well as individual oral feedback during times allocated to work on the exercises during the classes. Collective feedback will also be provided on the assignments handed in by the students.

Credit
7,5 ECTS
Type of assessment
Written assignment, 5 days under invigilation
individual take-home exam. It is not allowed to collaborate on the assignment with anyone. The exam is given in English and must be answered in English.
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Exam registration requirements

Full participation at the summerschool is mandatory and the student must actively participate in all activities (lectures, exercises and computer tutorials, group work etc.).

Two mandatory assignments will have to be approved during the summer school to be allowed to sign up for the final exam.

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Aid
All aids allowed
Marking scale
Censorship form
No external censorship
for the written exam. The exam may be chosen for external censorship by random check.
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Exam period

The exam takes place

from July 20 at 10 AM to July 25 at 10 AM, 2020

from July 22 at 10 AM to July 27 at 10 AM, 2020

The summer school and the exam has been canceled.

Exam information:

In special cases, the exam date can be changed to another day and time within the exam period.

For enrolled students more information about examination, rules etc. is available at Master students (UK) and Master students (DK).

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Re-exam

The reexam takes place

in the period December 2020 - January 2021

The summer school and the exam has been canceled.

Reexam information:

If only a few students have registered the reexam it might change to oral including the date, time and place, which will be informed in KUNet or by the Examination Office.

More information is available at Master students (UK) and Master students (DK).

##### Criteria for exam assesment

Students are assessed on the extent to which they master the learning outcome for the course.

To receive the top grade, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.