Course material for ECON2148 : Optimization-Conscious Econometrics
Autumn 2020, Harvard University
Tuesday-Thursday, 3:00-4:15 am. NEW TIME!
This course studies the core optimization concepts underlying econometric estimation and inference. The objective is to both develop a deep understanding of how estimators are computed, and to get a better theoretical and geometrical understanding of classical econometric estimators through the prism of optimization theory.
Each optimization concept or method is studied using a well established econometric estimator as the working example: linear programming is taught through the example of quantile regression, duality is taught via nonparametric inference, numerical linear algebra is taught via partial identification questions in OLS, integer programming is taught as a solution method for instrumental variables quantile regression, and so on.
Lecture 0: Markov Chain Monte Carlo Methods
Lecture 1: Quantile Regression and Linear Programming
Lecture 2: Nonparametric Robust Inference and Duality -- updated Feb 19, 2021
Lecture 3: Optimal Transport and Network Flow Problems -- updated Feb 19, 2021
Lecture 4: Regularized Optimal Transport, Sinkhorn's Theorem, and Iterative Projections -- updated Fab 19, 2021
Lecture 5: Integer Programming and Instrumental Variables Quantile Regression
Lecture 6: Numerical Algebra and Partial Identification in Ordinary Least-Squares with Singular Design Matrix -- preliminary
Lecture 7: Lagrange Multiplier Theory and the Degrees of Freedom of Constrained Estimators
Lecture 8: Numerical Integration and Uniform Inference
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Recorded lecture, September 3, 2020
Recorded lecture, September 8, 2020
Recorded lecture, September 10, 2020
Recorded lecture, September 15, 2020
Recorded lecture, September 22, 2020
Recorded lecture, September 24, 2020
Recorded lecture, September 29, 2020
Recored lecture, October 1, 2020
Recorded lecture, October 6, 2020