Econometrics and Statistical Methods
Econometrics research and teaching at MIT blends the theory and practice of economic data analysis. The study of econometrics has direct roots in theoretical statistics, but it finds application in a wide range of topics in both microeconomics and macroeconomics.
Victor Chernozhukov carries out research on a range of topics in econometric theory including decision-theoretic and Bayes alternatives to generalized method of moments estimation, extreme value theory and quantile regression. He is also applying these techniques to economic problems, often in collaboration with other MIT faculty or students. Jerry Hausman has made fundamental contributions to the econometric analysis of microeconomic data, developing new ways to estimate models of transportation, labor supply, research and development, the return to education, and stock market prices. He is currently investigating measurement error in models of discrete choice, while carrying out a range of applied studies in industrial organization and public finance. Anna Mikusheva works on time series econometrics issues. Her research has emphasized the problems of statistical inference when time series are nearly non-stationary. Whitney Newey also works on micro-econometrics, where he has developed methods for checking the validity of, and for weakening maintained assumptions of, statistical and economic models. His recent interests include finding improved methods for inference as well as studying the effects of tax reform on labor supply.
In addition to these core econometrics faculty members, several other faculty members in the Economics Department and the Sloan School have important interests in econometrics. Joshua Angrist studies methods for program evaluation in applied economics. Andrew Lo of the Sloan School studies the econometrics of financial markets, and is a co-author of a leading text in this field. Thomas Stoker, another member of the Sloan School faculty, has also worked on a range of problems in micro-econometrics.
The graduate econometrics course sequence gives students the best tools available for solving difficult statistical problems. The courses cover standard topics such as linear regression, while also introducing students to the latest techniques for empirical research. Course material is updated regularly to reflect advances in the field. There is a weekly MIT-Harvard econometrics seminar.