Advanced Microeconometrics

Course content

The overall purpose of the course is to provide a fundamental understanding of microeconometric methods and their application. These methods consist of behavioral models and statistical techniques to estimate these models.

The course will cover the following methods of estimation:

  • Estimation under unconfoundedness

  • Instrumental variable estimation

  • Linear panel data methods

  • Regression discontinuity design

  • Control function approaches

  • Non-linear estimation methods and numerical optimization

  • High-dimensional models

  • Discrete response models

  • Corner solution models and censored data

  • Non-parametric estimation
Education

MSc programme in Economics – elective course

Learning outcome

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

Knowledge:

  • The course will introduce students to the counterfactual set-up and the key treatment parameters we seek to estimate.

  • Students should understand how the estimated parameters rely on specific identifying assumptions.

  • Students should understand the principles of M-estimation in terms of estimation and inference as well as key examples of M-estimators.

  • Students should know how the most common numerical optimizers work.

  • Students should understand which estimator to use depending on the nature of the data (discrete, corner solution, censoring, sample selection, …).

  • Students should understand how to exploit panel data both for linear models and in non-linear settings.

Skills:

  • Students should be able to discuss the identifying assumptions and use regressions or descriptive data analysis to assess the assumptions.

  • Students should be able to implement an empirical policy evaluation analysis.

  • Students should be able to take an estimator from an academic paper or book, code it up in Matlab and estimate parameters as well as obtain standard errors.

Competences:

  • Students should learn how to exploit variation induced by a policy to set-up a credible research design.

  • When faced with a new dataset (whether in academia or in the real world), students should be able to

    • assess which estimator will be best suited to answer a given question,

    • code up the estimator an estimate parameters,

    • and test statistical hypotheses.

  • Students should learn how to develop arguments supporting an identification strategy.

  • Students should learn how to assess the identification strategies in existing research papers as well as in their own analyses.

  • The acquired skills in microeconometric theory and practice provide a strong background that enable students to do empirical analyses at a high level suitable for the master thesis, but also relevant for answering empirical economic questions that could be encountered in a government agency or in the private sector.

 

The lectures cover the theory and intuition of the estimators and methods. In the exercise classes, students obtain hands-on coding experience with implementing the estimators on real datasets primarily in Matlab.

Angrist, J.D. and J.-S. Pischke (2009), “Mostly Hamless Econometrics”, Princeton University Press, Princeton, New Jersey, ISBN: 978-0-691-12035-5.

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

Lecture notes and slides

Pre-requisites are the bachelor-level econometrics couses, Econometrics I and II (formerly Econometrics B and C). Prior knowledge about programming in Matlab is not required. Note that you will be required to do mathematical derivations for the exam.

Timetable and venue:
To see the time and location of classroom please press the link under "Se skema" (See schedule) at the right side of this page (16E means Autumn 2016).

You can find the similar information partly in English at
https:/​/​skema.ku.dk/​ku1617/​uk/​module.htm
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-E16; [Name of course]””
-Select Report Type: List
-Select Period: “Efterrår/Autumn – Weeks 30-3”
Press: “ View Timetable”

Please be aware regarding exercise classes:
- That the schedule of the exercise classes is only a pre-planned schedule and that it can be changed until just before the teaching begins without the participants accept. If this happens the participants will be informed or can see it at the above link. After enrollment it can be seen in KUnet and by the app myUCPH.
- That if too many students have wished a specific class, students will be registered randomly at another class. It is not possible to change class after the registration period has expired, unless the registration clashes with another course registration.
- That if not enough registered students or available teachers the exercise classes may be jointed.
- That it is not allowed to participate in an exercise class the student is not registered.
- That all exercise classes will be taught in English.

ECTS
7,5 ECTS
Type of assessment
Oral examination, 20-25 minuts under invigilation
The assessment is based on an oral exam without preparation.
The exam can be in English or in Danish. Language must be chosen at the course registration
Aid
Without aids
Marking scale
7-point grading scale
Censorship form
External censorship
100% censorship
Criteria for exam assessment

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

To receive the top grade, the student must be able to demonstrate in an excellent manner that he or she has acquired and can make use of the knowledge, skills and competencies listed in the learning outcomes.

So in order to obtain the grade 12, students should meet the following criteria:

Knowledge:

  • The student should be able to give a detailed account of the estimators in the course.

  • The student should be able to derive the estimator and other relevant statistics, including how standard errors are obtained.

  • The student should be able to describe how the estimation is conducted.

Skills:

  • The student should be able to discuss the use of an estimator in an empirical context.

  • For likelihood models, the student should be able to write up the data generating model and derive the likelihood function.

Competencies:

  • Students should be able to select a suitable estimator for answering an empirical question.

  • Students should be able to present arguments for or against a given research strategy.

     

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 42
  • Class Exercises
  • 24
  • Preparation
  • 139,7
  • Exam
  • 0,3
  • English
  • 206,0