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Astonishing recent progress in numerical optimization presents many opportunities and challenges for modern data analysis and scientific inference.  Econometrics and statistics offer a valuable angle from which to study optimization problems and, conversely, operations research offers a productive perspective from which to consider many problems in econometrics and statistics, whence the importance of fostering interactions between academic communities.

This conference aims to bring together scholars from economics, applied mathematics, operations research, computer science and statistics to share their experience and expertise on these important issues.



Friday, November 15, 2019
8:00 am - 8:25 am
Registration and Breakfast
8:25 am - 8:30 am
Introductory Remarks

Session 1: Modern Optimization in Econometrics

8:30 am - 9:15 am
Nonparametric Maximum Likelihood Methods for Binary Response Models with Random Coefficients
Jiaying Gu, University of Toronto
9:15 am - 10:00 am
L0 Penalized Quantile Regression
Simon Lee, Columbia University
10:00 am - 10:45 am
Nonlinear Earnings and Employment Dynamics at the Extensive and Intensive Margins
10:45 am - 11:30 am
Deep Inference: Artificial Intelligence for Structural Model Estimation
Elena Manresa, New York University
11:30 am - 12:15 pm
Recovering Latent Variables by Matching
Stephane Bonhomme, University of Chicago
12:15 pm - 1:00 pm

Session 2: Polynomial Optimization

1:00 pm - 1:45 pm
Polynomial Optimization and Symmetry
Annie Raymond, University of Massachusetts
1:45 pm - 2:30 pm
The Moment-SOS Hierarchy: with Applications in Optimization, Probability, Statistics, Control, Non-Linear PDEs, Computational Geometry
Jean-Bernard Lasserre, LAAS-CNRS, Toulouse
2:30 pm - 2:45 pm
Identification of Linear Models Using Polynomial Optimization
Wooyong Lee, University of Chicago
2:45 pm - 3:15 pm

Session 3: Questions at the Intersection of Optimization and Econometrics

3:15 pm - 4:00 pm
Algorithmic Sampling from an Econometric Perspective
Serena Ng, Columbia University
4:00 pm - 4:45 pm
Michael Jordan, University of California, Berkeley
4:45 pm - 5:25 pm
Stephen Wright, University of Wisconsin
Adjourn for the Day
Saturday, November 16, 2019
8:00 am - 8:30 am

Session 3, cont.: Questions at the Intersection of Optimization and Econometrics

8:30 am - 9:15 am
Elie Tamer, Harvard University

Session 5: Integer Programming, Econometrics and Statistics

9:15 am - 10:00 am
10:00 am - 10:45 am
Structured Statistical Learning at Scale: Convex and Mixed Integer Optimization Perspectives
10:45 am - 11:30 am
Advances at the Intersection of Integer Programming, Data Science, and Econometrics
Andrea Lodi, Polytechnique Montreal
11:30 am - 12:15 pm
Building Representative Matched Samples with Multi-valued Treatments in Large Observational Studies
Jose Zubizarreta, Harvard University
12:15 pm - 1:00 pm

Session 6: Linear Programming and Econometrics

1:00 pm - 1:45 pm
Minimax Regret Estimation of Hedonic Models: a Linear Programming Approach
Alfred Galichon, New York University
1:45 pm - 2:30 pm
Differentiable Ranks and Quantiles Using Optimal Transport
Marco Cuturi, Google / CREST - IPP
2:30 pm - 3:15 pm
Nonparametric Estimates of Demand in the California Health Insurance Exchange
Alexander Torgovitsky, University of Chicago
3:15 pm - 3:30 pm
Linear Programming Aspects of Regression Rankscore Inference
Yuehao Bai, University of Chicago
3:30 pm - 3:45 pm

Session 7: Stochastic Programming and Econometrics

3:45 pm - 4:30 pm
Inference by Stochastic Optimization: A Free Lunch Bootstrap
Jean-Jacques Forneron, Boston University
4:30 pm - 5:15 pm
Spatial Price Integration in Competitive Markets with Capacitated Transportation Networks
John Birge, University of Chicago
5:15 pm - 5:20 pm
Closing Remarks
Conference Concludes