You’ll have the option of taking courses that address your individual career choices. Samples include:
- Applied Regression Analysis - Regression is a powerful and widely used data analysis technique used to analyze a variety of complex, real-world problems. Topics covered include: (1) review of simple linear regression; (2) multiple regression (understanding the model, model specification and casual inference, interpreting the coefficients, R-squared, t- and F-tests, model diagnostics, model building, model selection); (3) time series (autocorrelation functions, auto regression, prediction); (4) logistic regression.
- Analysis of Financial Time Series - This course focuses on the theory and applications of financial time series analysis, especially in volatility modeling and risk management. Examples of topics covered include asset returns, business cycles, bid-ask bounce, nonlinear financial data, Black-Scholes pricing formulas, and more.
- Financial Econometrics - The topics covered are of real-world, practical interest and are closely linked to material covered in other advanced finance courses. Topics covered include ARMA models, volatility models (GARCH), factor models, issues in the analysis of panel data, and models for transactions data and the analysis of transactions cost.
- Statistical Insight into Marketing, Consulting, and Entrepreneurship - You decide to establish a start-up in marketing consulting. You search the internet and find, to your dismay, well over 650 companies in that area - each one claiming to be the best and unique. In order to compete in this arena you need to have the ability to identify upcoming trends and new problems in the marketing area, AND to be able to provide original, sound, fast, and applicable solutions to these problems. Unlike marketing research, marketing consulting is a problem-solving endeavor that requires a great deal of specificity and is fueled by experience. This course is meant to give future consultants and entrepreneurs important tools and ways of thinking that are relevant for dealing with insightful consulting and are useful in the practice of marketing consulting.
You'll study with professors who conduct groundbreaking research and share their experience developing statistical methods to analyze economic and business problems.
- Federico M. Bandi, studies financial econometrics, time series econometrics, continuous-time asset pricing, empirical asset pricing, and empirical market microstructure. Bandi serves as associate editor of Econometric Theory, the Journal of Business and Economic Statistics, and the Journal of Financial Econometrics.
- Alan Bester, studies estimation and interference in dynamic econometric models and applications to finance. He is a referee for a number of journals, including, the Journal of Econometrics, the Journal of Business and Economic Statistics, and Journal of Financial Econometrics.
- Carlos M. Carvalho, studies Bayesian statistics in complex, high-dimensional problems with applications ranging from finance to genetics. His current projects include research on large-scale factor models, graphical models, Bayesian model selection, particle filtering, and stochastic volatility models.
- Christian B. Hansen, studies applied and theoretical econometrics, efficient estimation of panel data models, quantile regression, weak instruments, empirical public finance, and labor economics. In 2006, he was named an IBM Scholar. Hansen's articles have appeared in Econometrica, the Review of Economics and Statistics, and the Journal of Econometrics.
- Nicholas Polson, conducts research on Markov Chain Monte Carlo methods, financial econometrics, and Bayesian interference. His articles have appeared in a number of academic journals, such as the Journal of Risk Finance and the Journal of Royal Statistical Society, as well as such mainstream publications as the Wall Street Journal and Chance.
- Jeffery R. Russell, conducts research on financial econometrics, time series, applied econometrics, empirical market microstructure, and high-frequency financial data. Russell's recent research has focused on using intraday price data to measure and predict financial asset volatility. His research is supported by a Morgan Stanley Equity Microstructure Grant and he is the recipient of an Alfred P. Sloan Doctoral Dissertation Fellowship.
- Matt Taddy, considers data analysis applications in ecology, medicine, engineering, econometrics, and social research. This applied work involves extensive collaboration with large research agencies, including Lawrence Livermore National Laboratory, Sandia National Laboratories, NASA Ames Research Center, and Los Alamos National Laboratory.
- Ruey S. Tsay, studies how to find the dynamic relationships between variables and how to extract information from messy data. He has received nine National Science Foundation grants and holds a US patent for a system and method for building a time series model.