Introductory Econometrics for Business and Economics

COURSE OBJECTIVE
By the end of this course students will have had an introduction to modern econometric techniques, that will enable them to conduct an
empirical study of their own. In particular, students will be familiar with econometric methods for cross-sectional and panel data, and with
real-world applications in macroeconomics, finance and business.

COURSE CONTENT
First, a review is given of least squares estimation and testing in the simple linear cross-sectional regression model. We discuss the classical assumptions, and the consequences arising when these assumptions are not fulfilled. The linear model with multiple regressors is discussed using matrix notation. Furthermore, we cover maximum likelihood estimation, and models that are nonlinear in variables. Finally, an introduction to panel data analysis is given.
Throughout the course, the focus lies on developing an intuition for state-of-the-art econometric concepts. A balance is struck between
theoretical derivations and empirical applications. Extensive use is made of the statistical software R, both for in-class illustration and for hands-on exercises. An introduction to R is provided in the tutorial of the first week.

TEACHING METHODS
Lectures (4h per week) and tutorials (2h per week).

TYPE OF ASSESSMENT
Final written exam (85%) and practical assignment (15%)

RECOMMENDED BACKGROUND KNOWLEDGE
This 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.