BUS444E Econometrics for Business Research

• 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, and particularly to estimate causal effects. 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, and to competently interpret other people's data analysis. Practical experience in uploading data, creating tables and running regressions is a crucial part of this, and therefore covered in the course. But the most difficult part of econometrics is to know how to interpret regression results. This requires a thorough conceptual understanding. Although we use formal and rigorous reasoning, the mathematics involved is quite basic.

  In addition to practice with real world business-related data, the main topics covered are:

  • Review of probability (expectation, variance, covariance, conditional expectation, linear projection)
  • OLS (ordinary least squares with one explanatory variable)
  • Properties of OLS (consistency, asymptotic normality, standard errors)
  • Identification (causation vs. correlation, exogeneity)
  • OLS with several explanatory variables
  • Statistical inference (hypothesis testing, p-value, confidence intervals)
  • Nonlinear transformations of the data (log models, binary variables)
  • Other data structures (difference-in-differences, panel data, cluster samples, time series)
  • Instrumental variables (2-stage least squares, supply and demand)

• 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 can:

  Knowledge

  • Explain the assumptions empirical analyses are based on.
  • Describe the difference between prediction and estimation of causal effects.
  • Understand how to interpret regression results.

  Skills

  • Identify, collect, and organize relevant data.
  • Analyze data using standard methods for cross section data.
  • Interpret results from empirical analyses and identify potential weaknesses.

  General competence

  • Determine whether a given set of regression results can be given a causal interpretation.
  • Formulate an empirical research question.
  • Structure a master thesis.
  • Use econometric methods in own work, for example as part of the methodology in a master thesis.

• Teaching

  The teaching consists of lectures and student activities (problem sets, data exercises, mock exams, discussion). Lectures on technical material will be streamed and recorded.

• Recommended prerequisites

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

  Knowledge of R is not required.

• Credit reduction due to overlap

  The course cannot be combined with ECN402, BAN431, BUS444, BUS444N, or FIE401(A/B)

• Compulsory Activity

  Compulsory assignments:

  Two individual problem sets / data exercises (graded approved/not approved).

• Assessment

  A 3-hour written school exam (pen and paper).

• Grading Scale

  A-F

• Computer tools

  R, RStudio

• Literature

  The main reading is a set of lecture notes that will be provided on Canvas at the start of the semester.

  It is not necessary to buy a textbook, but you may find it useful to get an alternative exposition of the material, additional problems, etc.

  Recommended textbook:

  Stock and Watson: Introduction to Econometrics, 4th edition

  Alternative textbook:

  Wooldridge, J.: Introductory Econometrics, 7th edition (slightly more technical than Stock and Watson but overall similar)

  If time: one or two academic articles on business topics.

• Permitted Support Material

  Calculator

  One bilingual dictionary (Category I)

  All in accordance with Supplementary provisions to the Regulations for Full-time Study Programmes at the Norwegian School of Economics Ch.4 Permitted support materialhttps://www.nhh.no/en/for-students/regulations/https://www.nhh.no/en/for-students/regulations/and https://www.nhh.no/en/for-students/examinations/examination-support-materials/https://www.nhh.no/en/for-students/examinations/examination-support-materials/