96796 - BUSINESS STATISTICS: METHOD AND APPLICATIONS

Learning outcomes

At the end of the course the student knows the main applied statistical methods to extract useful information from business databases and to support the management decision process in the face of uncertainty. In particular the student is able to apply statistical indicators and linear/non-linear regression analysis for the evaluation of firms’ performance, the study of customers’ behavior and the choice of an investment project, by using a statistical software. At the end of the course the student is able: - to select and apply the most appropriate methodology to quantitatively analyze the business phenomena; - to critically interpret empirical results.

Course contents

By the end of this course, you should be able to: 1. Explain what is meant by business statistics, the difference between risk and uncertainty and why they play a fundamental role in the world of business; 2. describe the different kinds of market structure and use appropriate data sets to measure market concentration. Be able to combine this with other analysis to provide a critique of the degree of competition in a market and how it can affect a firm's pricing and marketing strategies; 3. explain the importance of randomised experiments for making causal inferences and their role in A/B testing for website optimisation from a frequentist perspective; 4. explain the challenges of making causal inferences for non-experimental data sets, such as with observational data or databases held by your firm; 5. explain the conditions under which a multiple regression framework can be used to estimate a causal effect; 6. explain the challenges posed to the business statistician by problems arising from endogeneity resulting from omitted variable bias, simultaneity bias and measurement error; 7. explain how natural experiments, instrumental variables and regression discontinuity designs can overcome the endogeneity problem and be able to use appropriate software to analyse appropriate data sets; 8. explain what is meant by Bayesian inference and decision theory and the value of information; 9. use the above methods to extract and analyse business-relevant information from appropriate data sets, present and report it in a form which provides clear and accessible decision-support to managers.

Readings/Bibliography

(Texts are recommended. NOTE: list is provisional and may be subject to change.)

Introduction to Econometrics, Global Edition. J. H. Stock and M. W. Watson. 4th edition. Pearson International Content, 2019.

Mostly Harmless Econometrics: An Empiricist’s Companion. J. D. Angrist and J.-S. Pischke. Princeton University Press, 2009.

Econometric Analysis of Cross-Sectional and Panel Data. J. M. Wooldridge. MIT Press, 2010.

Principles of Economics. S. A. Greenlaw and D. Shapiro. Openstax, 2011.

Microeconomic Theory: Concepts and Connections. M. E. Wetzstein. 2nd edition. Routledge, New York, 2013.

Teaching methods

Lectures and workshops, some computer-based.

Assessment methods

Written examination at the end of the course which will examine students on the learning aims and objectives that are set for the course and each individual class.

Teaching tools

The e-learning platform will provide access to lecture slides, case studies, data sets and solutions. Normally lessons will also be recorded and made available to support your studies. Weekly office hours will be held and comments and grading of the exams will be available in online feedback meetings.

Office hours

See the website of Martin Forster