BUS444E Econometrics for Business Research
Autumn 2023Spring 2024
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)
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
- know which assumptions empirical analyses are based on
- know the difference between prediction and estimation of causal effects
- know how to interpret regression results
- know how to structure a master thesis
- 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
- Are able to determine whether a given set of regression results can be given a causal interpretation
- 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
Lectures with some student activities (doing simple problems, discussion). Lectures will not be streamed or recorded.
Some shorter videos covering technical material may be provided.
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)
Ca. 3 individual problem sets / data exercises that will be graded approved/not approved.
A 3-hour written school exam.
The main reading is a set of lecture notes that will be provided on Canvas at the start of the semester.
It is not absolutely necessary to buy a textbook, but it is recommended (to give you an alternative exposition of the material, additional problems, etc.).
Stock and Watson: Introduction to Econometrics, 4th edition
Wooldridge, J.: Introductory Econometrics, 7th edition (slightly more technical than Stock and Watson)
A few academic articles on business topics.
Permitted Support Material
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 material
and https://www.nhh.no/en/for-students/regulations/ https://www.nhh.no/en/for-students/regulations/ https://www.nhh.no/en/for-students/examinations/examination-support-materials/ https://www.nhh.no/en/for-students/examinations/examination-support-materials/
- ECTS Credits
- Teaching language
Spring. Offered Spring 2023.
Associate Professor Øyvind Thomassen, Department of Business and Management Science