AØKK08106U Seminar: Advanced Microeconometrics

Volume 2017/2018
Education

MSc programme in Economics

The seminar is primarily for students at the MSc of Economics

 

Content

The primary goal of this seminar is to supplement the course Advanced Microeconometrics with hands-on experience in programming and estimating advanced microeconometric models. After taking the course and this seminar, students will be well prepared to conduct empirical research using modern tools that are becoming increasingly used and demanded both inside and outside of academia.

Students will write a seminar paper on a topic of their choice. They will be able to choose the estimation approach (frequentist or Bayesian), the estimator or simulationbased method, as well as the data (real or simulated) they would like to work with. The main topic has to be connected with the course Advanced Microeconometrics, and should be discussed and agreed with the instructor at the beginning of the semester.

For example, topics may be selected along one of the following lines:

  • Estimate an economic model using real data and an estimator derived and programmed from scratch.
  • Study and compare the statistical properties of competing estimators using Monte Carlo simulations.
  • Reproduce and/or extend the results of an article published in a scientific journal.

 

The most time-consuming part of this seminar is expected to be the coding of the estimator(s). Working in groups of 2-3 students is of great importance in the coding phase and, in particular, with bug fixing. For this reason, students will be asked to work in groups of 2 to 3 students (depending on the total number of participants).

Good communication and dissemination of the results is an integral part of the research process. For this reason, a workshop will be organized at the end of the semester to allow students to present and to discuss their projects. All students will participate in this workshop both as presenters and as opponents (each student will be allocated another project to discuss).

Learning Outcome

After completing the course, the student should be able to:

Knowledge

  • Have a sound overview of the relevant literature related to the selected topic, and understand the state of the art as well as the limitations of the current approaches.
  • Understand the theoretical foundations of the microeconometric methods used, as well as their practical implementation.

Skills

  • Pose a focused economic research question (inspired, for example, by an already published paper).
  • Find data that can be used to answer the question.
  • Estimate relevant models and test hypotheses using methods discussed in the course Advanced Microeconometrics.
  • Program the estimators implemented in the paper.
  • Investigate the properties of the estimators and tests using Monte Carlo techniques.
  • Write a short and focused seminar paper.
  • Make an oral presentation of the economic problem, econometric model and the actual implementation (code).

  • Discuss an empirical strategy as opponent.

Competencies

  • Conduct a full econometric analysis, from the selection of the estimation approach to its practical implementation, including programming and application to real or simulated data, as well as interpretation and testing of the results, and criticism of the chosen method.
  • Apply the knowledge and the skills learned during the course and the seminar to other economic problems. This will be particularly relevant to students planning on writing a Msc. Thesis or a PhD thesis in the future, but also to students willing to work in the private sector, who will need to apply these skills in practice.

Cameron, A. C. and P. K. Trivedi (2005), “Microeconometrics: Methods and Applications”, Cambridge University Press, ISBN: 978-0521848053.

Any other textbooks or scientific articles relevant for the topic selected by the students. To be discussed with the instructor at the beginning of the semester.

 

Since this seminar is connected to the course Advanced Microeconometrics, it is highly recommended to have successfully completed this course in order to attend the seminar.

Students who attended the Summer School in Bayesian Econometrics can also participate in this seminar, provided they work on a topic that overlaps with the course Advanced Microeconometrics.

Note that no introductory lectures will be given. Students are expected to master the methods learned in the course Advanced Microeconometrics, including their practical implementation (i.e., programming an estimator in MATLAB, application to simulated and real data).

Programming experience is required to attend this seminar. Students can write their code in MATLAB, like in the course Advanced Microeconometrics. As an alternative, the R programming language may be used (note that no programming languages other than MATLAB and R will be supported).
Kick-off meeting, research and writing process of the seminar paper, sessions with presentation of own paper and critical evaluation/feedback to another student´s paper, actively participating in discussions at class.

Before the session a "so-finalized-as-possible"-draft of the paper must be uploaded in Absalon. After the presentations, the student submit an edited version of the paper in the Digital Exam portal as the final exam paper. The aim is that students use the presentation sessions as an opportunity to receive and use the constructive feedback to improve the paper.
Schedule:

• Kick-off meeting: February 5, 10-12
Groups of students will be formed during or after the initial meeting, and should be communicated to the instructor one week after the initial class meeting at the latest.
• Deadline project description (commitment paper) by March 1st, 2018.
• Progress meetings: Individual supervision meeting will be proposed to each group of students to discuss potential problems with their work. To make the supervision meeting as productive as possible, students will have to send a two-page supervision document to the instructor at least one day before the meeting. This document should include a brief update on the project and a description of the current problems to be discussed with the instructor. (Note that the meeting will automatically be canceled if the supervision document is not sent in time.)
• Deadline of pre-paper uploaded to Absalon : a week before the workshop
• Workshop of oral presentations: Dates will be agreed on in collaboration with the students.
  • Category
  • Hours
  • Project work
  • 186
  • Seminar
  • 20
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Written examination
- a seminar paper in English that meets the formal requirements for written papers stated in the curriculum and at KUNet for seminars.
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Exam registration requirements

Attendance in all activities at the seminar as stated in the formal requrements in the Curriculum and at the  KUnet for seminars (UK) and Kunet for seminars (DK) is required to participate in the exam.

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Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
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Exam period

Deadline for uploading the final seminar paper to DE: 1st of June 2018 before 10:00 AM

 

Exam information:

For enrolled students more information about examination, rules, exam schedule etc. is available at the intranet for master students (UK) and  master students (DK)

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Re-exam

The reexam is a written paper as stated in the  Master curriculum and at the KUnet for seminars for master students (UK) and master students (DK). 

 

Exam information:

For enrolled students more information about re-examination, rules, re-exam schedule etc. is available at the intranet for  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 seminar and the objectives stated in the Curriculum.

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.