Covers key models as well as identification and estimation methods used in modern econometrics. Presents modern ways to set up problems and do better estimation and inference than the current empirical practice. Introduces generalized method of moments and the method of M-estimators in addition to more modern versions of these methods dealing with important issues, such as weak identification or biases arising in high dimensions. Also discusses the bootstrap and explores very high dimensional formulations, or "big data." Students gain practical experience by applying the methods to real data sets. Enrollment limited.
This class has 14.381 as a prerequisite.
14.382 will not be offered this semester. It will be available in the Spring semester, and will be instructed by V. Chernozhukov.
Lecture occurs 2:30 PM to 4:00 PM on Mondays and Wednesdays in E25-111.
This class counts for a total of 12 credits.
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