MARK 8397 Empirical Models with Business Applications (PhD)
Instructor: Ye Hu, yehu.mark8397@gmail.com
Office hours: by appointment
Office: 375K Melcher Hall
Classroom: Marketing Depart Conference Room
Introduction
Categorical data (as compared to continuous data) is often observed in business
and social science research. For instance, a company’s decision to go for Chapter
11 protection could be either “yes” or “no”; a consumer’s evaluation of a book at
Amazon is 1, 2, 3, 4, or 5 stars; the number of bottles of beer a consumer purchases
at a liquor store can only be a natural number instead of 1.2 bottles. When we analyze
such data, the continuous data analysis techniques (such as correlation, linear multiple
regression) may no longer apply. This course focuses on the applications of the most
commonly used categorical data analysis methods. For each topic, we’ll first cover
the statistical basics and apply it to analyze a dataset.
Topics
- Contingency Table Analysis
- Binary Outcomes
- Ordered Outcomes
- Multinomial Nominal Outcomes
- Censored Outcomes
- Count Outcomes
The grade will be based on homework, class participation, and a mini-project.
Software: SAS
Prerequisites: Linear Algebra, Graduate level Econometrics 1 or equivalent, Some programming experience
Data