List of Concentrations

Econometrics and Statistics

All aspects of business require using real-world information to make good business decisions. Econometrics and statistics provides a broad set of quantitative tools that extract information from observable data in order to test our beliefs about the real world - and make our decisions even better.

Chicago Booth has a rich and deep history of asking for proof to support an idea. Econometric and statistical tools provide the means for the quantitative analysis and testing of economic and business models. Here, you will learn to identify what information is and is not important, and you’ll gain the ability to quantify an answer so as to develop a measure of certainty and ultimately be confident in your decisions. What is the default rate on a security? Does the default rate change over time? What is the price impact of an earnings announcement? What will the GDP be next quarter? What variables determine demand for a product? All of these are questions that you will be able to address with econometric and statistical methods.

COURSE SAMPLING
FACULTY SAMPLING

COURSE SAMPLING
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: (i) review of simple linear regression; (ii) 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); (iii) time series (autocorrelation functions, auto-regression, prediction) (iv) 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, non-linear 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 advance 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 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.

FACULTY SAMPLING
You’ll study with professors who conduct groundbreaking research, collaborate with the entrepreneurial and private equity communities, and bring their own entrepreneurial experiences into the classroom.

	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.		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.
	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.		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.
	Tomothy G. Conley, studies applied econometrics and received a National Science Foundation grant. He often works on empirical questions in the fields of development economics and industrial organization. Most of his research involves spatial models used to study phenomena like diffusion of information, retail competition, and spillover effects.		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.
	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.		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 U.S. patent for a system and method for building a time series model.