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ECOM-R315 Advanced Econometrics 2, 5 cr 
Code ECOM-R315  Validity 01.01.2017 -
Name Advanced Econometrics 2  Abbreviation Adv Econom 2 
Scope5 cr   
TypeAdvanced studies
  GradingGeneral scale 
    Can be taken more than onceno
Unit Master's Programme in Economics 

Mika Meitz 

Target group 

Master’s Programme in Economics (Research Track). Open also to doctoral students in economics.


Annually in the second period

Learning outcomes 

After the course, the student should know the properties of the estimators introduced and be able to apply them and the related inferential procedures in empirical work. The course should also give a solid foundation for the study of more specialised microeconometric and time series methods. In addition, a sufficient level of proficiency in simulation-based and basic nonparametric methods should be reached to enable critical evaluation of the empirical results obtained by these methods.

Completion methods 

The course consists of lectures (24 hours) and exercise sessions (8 hours), where solutions to the homework assignments are discussed. The lectures and exercise sessions are not mandatory. The course is completed by (i) a written final exam and (ii) homework assignments. The homework assignments consist of both analytical and empirical exercises.


Advanced Econometrics 1

Recommended optional studies 

Knowledge of R (or some other matrix programming language) is useful.


This course covers a number of topics beyond the standard linear regression model, including estimation methods other than least squares, such as the generalised method of moments and maximum likelihood estimation. In addition, simulation-based methods as well as basic nonparametric inference widely employed in empirical econometric research are discussed. Specifically, the topics covered in the course include

  • The generalised method of moments 
  • Maximum likelihood estimation (basic concepts, asymptotic estimation theory, statistical inference) 
  • Simulation methods (Monte Carlo simulations, Bootstrap) 
  • Nonparametric methods (kernel density estimation, semi- and nonparametric regression)
Study materials and literature 

Lecture slides and other material assigned by the lecturer

Activities and teaching methods in support of learning 

All material related to the course is delivered through the Moodle area of the course, which also contains a discussion forum where students can discuss issues related to the course with each other and the teacher.

Assessment practices and criteria 

The grade on a scale from 0 (fail) to 5 is based on the points earned in the final exam. At least 40% of the homework assignments must be completed to take the exam.


Current and future instruction
Functions Name Type cr Teacher Schedule
registration period has not begun Advanced Econometrics 2  Lecture Course  Mika Meitz  29.10.20 -05.03.21

Future examinations
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