This course is discontinued

ECON5102 – Advanced Econometrics - Microeconometrics

Schedule, syllabus and examination date

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Course content

This is an advanced econometric course on modelling, estimation and testing of economic relations from microeconomic data. Two broad topics will be discussed: Limited Dependent Variables and Causal inference.

Limited Dependent Variables refer to the case when the outcome variable is at least partially qualitative in nature. Examples are discrete choice or situations when the dependent variable is partially observed due to censoring or selection.

The causal inference part of the course introduces the potential outcome framework. It is shown how standard latent variable discrete choice models can be related to this framework, and how these can be used to define policy relevant effects.

The course then discusses the estimation of such effects using social experiments, and techniques such as matching and propensity score methods, regression discontinuity designs, and difference-in-differences. Suitable-applications of single-equation and multi-equation models, using appropriate software, will be discussed to illustrate the methods.

General aspects of Panel data econometrics will not be covered by this course, but some of its topics are at the interface between panel data econometrics and microeconometrics.

The following topics will be discussed:

  • The Binary and Multinomial Response Models for unordered and ordered choices. The utility basis of such models.
  • Other Limited Dependent Variables Models, e.g., for models for Count and Duration Data.
  • Missing Data and Selection Problems. Censored and Truncated Regression models.
  • Switching regression (Roy model).
  • Basic Concepts of Causal Inference - Experimental data - Performing social experiments - Imperfect compliance.
  • Observational data with selection on observables - Regression and matching.
  • Observational data with selection on unobservables - Instrumental Variables, Regression Discountinuity Design, Difference-in-differences.

Learning outcome

Knowledge
You should know

  • the general background and motivation for microeconometric models, including potential outcomes, the random utility model and optimisation in the presence of non-negativity constraints
  • econometric modelling of discrete phenomena and of the modelling of censored and truncated variables
  • important applications in the analysis of microeconomic relationships
  • the estimation of policy relevant effects using non-experimental data
  • the econometric terminology, important estimation, identifying assumptions and test procedures of these models

Skills
You should be able to

  • estimate models and test their validity using an observation set of relevant variables and suitable software

Competence
You should

  • be able to read and understand project reports and journal articles that make use of the concepts and methods that are introduced in the course
  • be able to make use of the course content in your own academic work, for example in analyses that are part of the master’s- or PhD thesis

Admission

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

The subject is open for both Norwegian and international students.
Students who are admitted to study programmes or individual courses at UiO must each semester register which courses and exams they wish to sign up for in StudentWeb.
International applicants, if you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures for international applicants.

Overlapping courses

10 credits against ECON5115 - Microeconometrics
10 credits against ECON9102 - Advanced Econometrics - Microeconomtrics
3 credits against ECON5101/9101 - Advanced Econometrics - Time Series
3 credits against ECON5103/9103 - Advanced Econometrics - Panel Data

Teaching

Access to teaching

A student who has completed compulsory instruction and coursework and has had these approved, is not entitled to repeat that instruction and coursework. A student who has been admitted to a course, but who has not completed compulsory instruction and coursework or had these approved, is entitled to repeat that instruction and coursework, depending on available capacity.

Examination

The students will be evaluated on the basis of a portfolio assessment.

Examination support material

No examination support material is allowed.

Language of examination

You may submit your response in Norwegian, Swedish, Danish or English. If you would prefer to have the exam text in English, you may apply to the course administrators.

Grading scale

Students on master's level are awarded on a descending scale using alphabetic grades from A to E for passes and F for fail. Students who would like to have the course approved as a part of our phd-program, must obtain the grade C or better. Students on phd-level are awarded either a passing or failing grade. The pass/fail scale is applied as a separate scale with only two possible results.

Explanations and appeals

It is recommended to request an explanation of your grade before you decide to appeal.

Appeal

Explanation

The deadline to request an explanation is one week after the grade is published. For oral and practical examinations, the deadline is immediately after you have received your grade.

The explanation should normally be given within two weeks after you have asked for it. The examiner decides whether the explanation is to be given in writing or verbally.

Resit an examination

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.

Other

This course prepares for the Ph.D. program. It provides a head start for last-year master students who intend to continue with a Ph.D.

Facts about this course

Credits

15

Level

Master

Teaching

Spring 2012

Examination

Spring 2012

Teaching language

English