Target group 
Students of Master’s Programme in Economics (General Track). Open to other students as well. 
Timing 
First autumn term 
Learning outcomes 
After the course, the student should
 Be very familiar with the interpretation of and statistical inference in the linear regression model in the crosssectional context
 Be familiar with the properties of the ordinary least squares, instrumental variables and the generalised method of moments estimators
 Understand the basic properties of the maximum likelihood estimator and the related asymptotic tests
 Be able to critically read empirical economic research employing methods covered in the course, to identify their potential methodological problems, to compare alternative econometric model specifications and to assess the adequacy of empirical results
 Be able to apply the models and methods covered in the course in empirical research

Completion methods 
The course consists of lectures (24 hours) and exercises either in separate sessions or integrated into the lectures. The lectures and exercise sessions are not mandatory.
The course includes a written final exam and a number of assignments that affect the grading. The homework assignments consist of analytical and empirical exercises. The former familiarise the student with the theory and calculations typically required in the practical implementation of the methods, while the latter teach skills of undertaking an empirical research project, including data handling, programming and interpreting results. 
Prerequisites 
The student is assumed to be familiar with the basics of the linear regression model and the principles of statistical inference to the extent covered in the basic studies in statistics and a Bachelorlevel introductory course in econometrics. Familiarity with matrices is also assumed. 
Recommended optional studies 
Knowledge of R (or some other matrix programming language) is useful, although the basics needed for the practical implementation of the methods can be acquired during the course. 
Contents 
The course builds upon a Bachelorlevel introductory course in econometrics. A central goal is to deepen the knowledge on the linear regression model in various directions, including regression with instrumental variables and heteroskedastic errors. In addition, maximum likelihood estimation and the related asymptotic tests are introduced.
The course starts with a review of the linear regression model and the smallsample and asymptotic properties of the ordinary least squares estimator and statistical inference concerning its parameters. A large part of the course is devoted to the detection of and addressing violations of the basic assumptions of the linear regression model. In particular, statistical inference based on the ordinary least squares estimator under heteroskedastic or autocorrelated errors are considered. The instrumental variables and the generalised method of moments estimators, useful in the case of endogenous regressors as well as the method of maximum likelihood, widely applicable in econometrics, also introduced. Throughout the course, the emphasis is on the practical aspects of econometric modelling instead of the foundations of statistical inference. The models and methods are illustrated by means of Monte Carlo simulations and empirical applications. 
Study materials and literature 
In addition to the lecture slides, selected parts of Chapters 1–6 of the textbook by Verbeek (A Guide to Modern Econometrics, 5th edition; the 2nd, 3rd and 4th editions can be used as well), and the manuals of the R packages covered in the course are recommended. 
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. During the lectures, a classroom participation system, such as Presemo, may be used to facilitate posting and for answering activating questions. Solutions to the homework assignments or quizzes based on them on the Moodle area of the course are graded, and the solutions are discussed during the lectures or in special exercise sessions. 
Assessment practices and criteria 
The grade on a scale from 0 (fail) to 5 is based on the sum of points earned in the final exam and from the homework assignments and potential classroom activities. The points are scaled such that the maximum number of points is 100, of which 60 come from the final exam and 40 from the homework assignments and other activities. To pass the course, the student must earn at least 50 points in total and at least 30 points from the final exam. 