AØKA08084U Advanced Microeconometrics
MSc programme in Economics – elective course
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
Discrete response models
Corner solution models and censored data
- Non-parametric estimation
After completing the course, the student should be able to:
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.
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.
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.
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
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
-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.
for enrolled students. More information about registration, schedule, rules, courses etc. can be found at the student intranet (KUnet) for courses (English) andstudent intranet (KUnet) for courses (Danish).
Registration and information for prospective foreign speaking students, exchange students, Open University etc. please find more information at Study Economics.
- 7,5 ECTS
- Type of assessment
- Oral examination, 20-25 minuts under invigilationThe 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
- Exam registration requirements
- Without aids
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Exam period
The oral exam is in week 49 and 50 in 2016 (December 5 to 16). The exact date and time of the exam will be informed by the Exam Office.
From Week 6 to week 8, 2017. The exact date and time of the exam will be informed by the Exam Office.
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 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:
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.
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.
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.
- Class Exercises