Econometrics for Business Research

BUS444E Econometrics for Business Research

Autumn 2021

  • Topics

    Modern companies produce large amounts of data. These data can be valuable both as inputs to internal management and for analysts outside the company. The objective of this course is to give students an introduction to econometric methods useful for analyzing data. Students will learn basic tools for quantifying and interpreting economic relationships. The course mixes practical work with data in the software package R and a theoretical treatment of econometrics. Throughout, the emphasis is on the skills needed to do high-quality data analysis in practice.

  • Learning outcome

    After completing the course, students will be familiar with the Ordinary Least Squares (OLS) estimator and some related methods. Students will be able to interpret and explain results from econometric studies, and be able to conduct econometric studies of their own.

    After completing the course, students

    Knowledge:

    • know which assumptions empirical analyses are based on
    • know how to structure a master thesis

    Skills:

    • Are able to identify, collect, and organize relevant data
    • Are able to analyze data using standard methods for cross section data
    • Are able to interpret results from empirical analyses and identify potential weaknesses

    Competence:

    • Are able to formulate an empirical research question
    • Are able to use econometric methods in own work, for example as part of the methodology in a master thesis

  • Teaching

    Teaching

    Your physical presence on campus will not be required to take this course.

    The 4 weekly contact hours will typically be spent like this:

    1. Lectures. Regular lectures or prerecorded audio/screen lectures: 2
    2. Group presentations. Student groups present solutions to weekly problem sets: 1 - 1.5
    3. Q&A. Questions-and-answers session : 0.5 - 1

    Further details:

    Lectures. These are intended to give an overview of the week's topic(s). You can choose to read the relevant textbook chapter (or other material) before or after the lecture.

    Group presentations. The size of student groups will depend on the size of the class, but will most likely consist of 4 - 8 students. Each week the student groups prepare answers to problems covering the previous week's material, and present the solution to the class, either in a classroom (streamed) or digitally (in Zoom or Teams). Student groups are responsible for resolving any technical challenges in presenting the solution. (For instance, equations can be shown by screen sharing with the class if the presentation is done digitally.) The preparation of the weekly presentation is intended as a core part of learning in the course, but students have an individual responsibility to familiarize themselves with the material before group meetings.

    Q&A. These sessions will be used to clarify any problematic concepts or areas. For these sessions to be useful, it is crucial that students participate. This participation should take two forms:

    • Before the Q&A session (and preferably as early as possible), each student group should send one email to the lecturer about any difficulties or issues they would like the session to deal with (the more precise, the better). If a student has difficulty with a concept, they should raise it with their group first. If, after discussing it, the group is not able to reach a point where everyone understands the concept, it should be included in the pre-Q&A email.
    • During the Q&A sessions, students should take part in the discussion, especially on the points raised by the group itself.

  • Recommended prerequisites

    Basic knowledge of mathematics, probability and statistics, as acquired in a Business or Economics undergraduate degree.

  • Credit reduction due to overlap

    The course cannot be combined with ECN402, BAN431, BUS444, BUS444N, or FIE401A/B

  • Requirements for course approval

    Participation in at least 70% of the group presentations is required for course approval.

    This requirement is just a formality; it is assumed that each student will participate in all presentations, both for the sake of their own learning and in order to contribute to making this course a fruitful learning experience for everyone involved. Whether you find the course material difficult or easy, participation in group work will usually give new insights that you might not have reached on your own. If you feel that you understand everything perfectly already, there is no better way of testing and consolidating your understanding than helping other people (in your group) understand.

    At the end of their weekly presentation, each group should state the names of the students who contributed to preparing the group's solution that week. If a group member did not take active part (e.g. did not come to meetings or spent the meeting talking to friends on the phone) in preparing the solution, their name should not be included in the list. It is in the group's interest not to put the names of any free rider on the list.

    Having one's name on the list in a given week counts as participation in that week.

  • Assessment

    An individual term paper involving analysis of real data using methods covered in the course. [50% of the grade]

    An individual home exam of 5 hours. [50% of the grade]

    The term paper will be due at the end of the semester. The exact dates for the home exam and submission of the term paper will be announced by the start of the semester.

    Note that while group work forms an important part of learning in the course, assessment is individual.

  • Grading Scale

    A-F

  • Computer tools

    R, RStudio

  • Literature

    Wooldridge, J.: «Introductory Econometrics» 7th edition

    A few articles, mainly to provide background information for data sets.

Overview

ECTS Credits
7.5
Teaching language
English
Semester

Spring. Offered Spring 2021.

Course responsible

Associate Professor Øyvind Thomassen, Department of Business and Management Science