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The study of the underlying assumptions, techniques and applications of single and multiple equation regression analysis in economics.

Prerequisite(s): Basic Statistics, ECON 8306/ECON 4300, or permission. Not open to non-degree graduate students.

The study and application of modern quantitative techniques to problem-solving in economics and business. It is designed to help the student to translate verbal arguments in economics and business into their mathematical equivalents, to improve analytical skills, and to attain proficiency in marginal analysis, equilibrium analysis, static optimization, and comparative statics analysis. It covers topics such as exponential and logarithmic functions and their applications, linear algebra and its applications, derivatives and their applications, maximization of functions with one variable and multi variables, maximization with non negativity constraints, and integral calculus and its applications in economics and business. (Cross-listed with ).

Prerequisite(s): ECON 2200 and ECON 2220, or BSAD 8180.

The course will cover forecasting tools and applications applied to business settings. The first half of the course will cover traditional Econometric forecasting methods and the second half of the course will focus on predictive analytics models and machine learning. Time in the computer lab will be focused on teaching students how to implement the models discussed in lectures. (Cross-listed with ).

Prerequisite(s): ECON 8320 (or equivalent programming experience) or permission of instructor. Not open to non-degree graduate students.

The course will cover basic principles of programming languages, as well as libraries useful in collecting, cleaning and analyzing data to answer research questions. The course will utilize basic Economic principles and Econometric methods as inspiration for assignments and projects throughout the duration of the course, and will do so in a way that is accessible to non-Economists. This course is intended to introduce the student to the Python programming language as a tool for conducting data analysis. While the course uses Python, the student should be able to move to other languages frequently used in data analysis using the principles taught in this course.

Prerequisite(s): BSAD 2130 or equivalent; or instructor approval.

Econometrics is routinely taught as an application class using a 'black box' like Stata or SAS to perform calculations. This class takes a different approach. Using the Python programming language, we build all estimators from scratch. Additionally, we introduce numerous non-parametric and simulation techniques. This approach to econometrics results in a stronger understanding of statistical assumptions and methods, a better understanding of when a method is appropriate, and stronger programming techniques. Furthermore, a deeper understanding of the underlying mechanics provides the student the ability to program custom procedures not already built into popular software packages. As part of the course, students will work with a community partner to answer a real question with data; MBA students should consult with their advisor about this course satisfying the project-focused capstone requirement.

Prerequisite(s): A multivariate or regression analysis course such as ECON 8300, ISQA 9130 or STAT 8436, and a programming class such as ECON 8320 or equivalent programming experience; or instructor approval. Not open to non-degree graduate students.

*ECON 8300 Substitutes BSAD 8700

**ECON 8330 Substitutes BSAD 8800