# Econometrics and Statistics

All aspects of business require using real-world information to make good business decisions. Econometrics and statistics provide 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.

You’ll have the option of taking courses that address your individual career choices. Samples include:

• Business Statistics - This course covers statistical concepts needed for modern business applications. The goal is to learn to use statistical tools, along with problem solving and communication skills, to analyze data and make business decisions. These tools also form the foundation for Chicago Booth elective courses, particularly in marketing, economics, and finance.
• 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 - In order to compete in the arena of marketing consulting, 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.

Drew D. Creal

Drew D. Creal, associate professor of econometrics and statistics, studies time series econometrics and statistics with particular emphasis on state space and time-varying parameter models. His research interests include applications in macroeconomics and finance.

Max Farrell

Max Farrell, assistant professor of econometrics and statistics, studies econometric theory and applied econometrics. His research focuses on model selection, high-dimensional data, and robust semiparametric methods, with a focus on increasing reliability and implementability in data analysis. His publications appear in the Journal of Econometrics and Advances in Econometrics, as well as a variety of health care and medical journals.

Robert Brandon Gramacy

Robert Brandon Gramacy, associate professor of econometrics and statistics, studies Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty. His application areas of interest include spatial data, sequential computer experiments, ecology, epidemiology, finance, and public policy.

P. Richard Hahn

P. Richard Hahn, assistant professor of econometrics and statistics, conducts research that develops computational methods for modeling complex real-world data, with a focus on behavioral data. He has developed methods and models for analyzing such diverse data as corporate accounting time series, handwriting samples, and behavioral game theory experiments.

Christian B. Hansen

Christian B. Hansen, Wallace W. Booth Professor of Econometrics and Statistics, 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.

Mladen Kolar, assistant professor of econometrics and statistics, focuses his research on high-dimensional statistical methods, graphical models, varying-coefficient models, and data mining, driven by the need to uncover interesting and scientifically meaningful structures from observational data. Kolar was awarded a prestigious Facebook Fellowship in 2010 for his work on machine learning and network models.

Robert E. McCulloch

Robert E. McCulloch, Katherine Dusak Miller Professor of Econometrics and Statistics, studies Bayesian methods, statistical analysis, and machine learning. His research has been published in a variety of publications, including Statistical Science, Journal of the American Statistical Association, Journal of Econometrics, Journal of Marketing Research, and Marketing Science. Additionally, he serves at the associate editor for both the Electronic Journal of Statistics, and the Journal of the American Statistical Association.

Nicholas Polson

Nicholas Polson, Robert Law, Jr., Professor of Econometrics and Statistics, 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

Jeffery R. Russell, Alper Family Professor of Econometrics and Statistics, 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, associate professor of econometrics and statistics and Neubauer Family Faculty Fellow, 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

Ruey S. Tsay, H.G.B. Alexander Professor of Econometrics and Statistics, 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.

Jing Cynthia Wu

Jing Cynthia Wu, assistant professor of econometrics and statistics, studies econometrics, monetary economics, and asset pricing. Her research interests include the term structure of interest rates, monetary policy, financial crises, and commodity futures markets. Her work helps unravel complicated term structure models and develops a straightforward framework for identification, estimation, and specification testing. Applying term structure models to monetary policy and commodity futures markets, she contributes important insights to the current literature on “quantitative easing” when the policy rate is at the zero lower bound, and the debate between policy makers and academia on the impact of index fund investment on the commodity futures prices.

Dacheng Xiu

Dacheng Xiu, assistant professor of econometrics and statistics, studies financial econometrics with an emphasis on exploring high-frequency financial data. His work has appeared in the Journal of Econometrics and the Journal of the American Statistical Association. His publication “Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data” has received the 2010 IMS Laha Award. His recent research interests also include empirical asset pricing and nonlinear time series.

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