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## Introduction to Econometrics

2018-2019

### Course Objective

By 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 Content

A 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
statistical software Stata, both for in-class illustration and for
hands-on exercises.

### Teaching Methods

Interactive lectures, theory exercises, and exercises in the computer
lab.

### Method of Assessment

Final written exam (85%) and practical assignment (15%)

### Literature

Main reference: Stock and Watson (2010), "Introduction to Econometrics",
Supplementary literature: Wooldridge (2013), "Introductory Econometrics:
A Modern Approach", Cengage Learning, Inc. 4th edition or newer.

### Target Audience

The 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.

Participation in this course is a worthwhile preparation for the
remaining courses of the Minor "Applied Econometrics: A Big Data
Experience for All.

### Explanation Canvas

All materials (slides, theory exercises, practice exams, etc.) are
provided on Canvas.

### 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
Stock & Watson, and students are recommended to
refresh their memory prior to the first lecture.

### General Information

Course Code E_EOR3_IE 6 EC P1 300 English School of Business and Economics dr. J. Schaumburg dr. J. Schaumburg

### Practical Information

You need to register for this course yourself

Teaching Methods Seminar, Lecture
Target audiences

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