Course ObjectiveBy the end of this course students will have had an introduction to
modern econometric techniques, that will enable them to conduct
methodological or empirical analyses of their own. In particular,
students will be familiar with both econometric theory and with
real-world applications in macroeconomics, finance and business.
Course ContentA review will be given of estimation and testing in the linear
cross-sectional regression model. We will discuss the classical
assumptions, and the consequences arising when these assumptions are not
fulfilled. Throughout the course, the focus will lie on developing an
intuition for state-of-the-art econometric concepts. A balance will be
struck between theoretical derivations and empirical applications. The
textbook used (see below) is particularly well-suited for this purpose,
as it is targeted at an audience of advanced undergraduate students in
economics and business studies. Extensive use will be made of the
statistical software R, both for in-class illustration and for hands-on
Teaching MethodsInteractive lectures, theory exercises, and practical exercises in the
Method of AssessmentFinal written exam (85%) and practical assignment (15%)
LiteratureMain reference: Stock and Watson (2010), "Introduction to Econometrics",
Pearson, 3rd edition or newer.
Supplementary literature: Wooldridge (2013), "Introductory Econometrics:
A Modern Approach", Cengage Learning, Inc. 4th edition or newer.
Target AudienceThe course is part of the SBE faculty minor "Applied Econometrics: A Big
Data Experience for All". It is targeted at students who are currently
not enrolled in Bachelor in Econometrics or a similar study program.
Additional InformationParticipation in this course is a worthwhile preparation for the
remaining courses of the Minor "Applied Econometrics: A Big Data
Experience for All".
Explanation CanvasAll materials (slides, theory exercises, practice exams, etc.) are
provided on Canvas.
Recommended background knowledgeThis course assumes familiarity with probabilistic concepts such as
discrete and continuous random variables, conditional expectations,
hypothesis testing and central limit theorems, with the basics of matrix
calculus, and with the essentials of regression analysis. This material,
excluding matrix calculus, corresponds more or less to chapters 1-5 in
the book by Stock/Watson (see literature references), and students are
recommended to refresh their memory prior to the first lecture.
|Language of Tuition||English|
|Faculty||School of Business and Economics|
|Course Coordinator||dr. J. Schaumburg|
|Examiner||dr. J. Schaumburg|
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Study Group, Computer lab, Lecture|
This course is also available as: