COURSE DESCRIPTION

The purpose of this course is to introduce econometric and statistical methods of relevance to business and, in particular, empirical finance.  Discussion is centered on models and methods applied to financial data on stock and portfolio returns.  The techniques are introduced and explained through lectures, in-class computer demonstrations, and student projects.  Although hands-on learning is key, the relevant statistical theory for estimation and model comparison is also introduced. As needed, both the frequentist and Bayesian aspects of that theory are presented.  The course should be valuable to a variety of students, including those with a primary interest in finance, marketing, operations, and accounting.

LEARNING OBJECTIVES

· Develop an understanding of modern statistical methods that are useful in business and empirical finance applications

· Develop an understanding of models relevant for time series and panel data, such as seemingly unrelated regressions and hierarchical models

· Develop an understanding of how models can be compared by marginal likelihoods and Bayes factors

· Develop an understanding of how out-of-sample predictions are generated by the sampling the future method and evaluated by predictive likelihoods

· Develop an understanding of these techniques through applications in empirical finance and other areas of business

· Develop an understanding of R, an open-source, objected oriented software and programming environment for doing statistics and data analysis

TEXT/MATERIALS

Recommended:

Richard Cotton (2013), “Learning R,” O’Reilly, New York

CANVAS

The website for this course is on the Canvas system. https://mycanvas.wustl.edu/

ASSIGNMENTS and GRADING

Your grade will be determined on the following:

ASSIGNMENT TOPIC

WEIGHT

Assignments

Class participation (in class assignments)

Project

30%

10%

20%

Final exam

40%

Total

100%

Attending class is very important because much will be learnt every time we meet.  Please keep up with the material and try not to fall behind.  If you are diligent with the textbook, with R and the assignments, you will do well.