Event·Nov 15, 2019, 12:00 AM·University of Chicago, Saieh Hall for Economics, Rm 021
Optimization-Conscious Econometrics Conference
Video
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 brought together scholars from economics, applied mathematics, operations research, computer science and statistics to share their experience and expertise on these important issues.
ORGANIZERS
Agenda
Friday, November 15, 2019
08:00:00–08:25:00
Registration and Breakfast
08:25:00–08:30:00
Introductory Remarks
Session 1: Modern Optimization in Econometrics
08:30:00–09:15:00
Nonparametric Maximum Likelihood Methods for Binary Response Models with Random Coefficients
09:15:00–10:00:00
L0 Penalized Quantile Regression
10:00:00–10:45:00
Nonlinear Earnings and Employment Dynamics at the Extensive and Intensive Margins
10:45:00–11:00:00
Break
11:00:00–11:45:00
Deep Inference: Artificial Intelligence for Structural Model Estimation
11:45:00–12:30:00
Recovering Latent Variables by Matching
12:30:00–13:15:00
Lunch
Session 2: Polynomial Optimization
13:15:00–14:00:00
Polynomial Optimization and Symmetry
14:00:00–14:45:00
The Moment-SOS Hierarchy: with Applications in Optimization, Probability, Statistics, Control, Non-Linear PDEs, Computational Geometry
14:45:00–15:00:00
Identification and Estimation of Dynamic Random Coefficient Models
15:00:00–15:30:00
Break
Session 3: Questions at the Intersection of Optimization and Econometrics
15:30:00–16:15:00
Algorithmic Sampling from an Econometric Perspective
16:15:00–17:00:00
Optimization, Diffusion, and Dimension Dependence
17:00:00–17:45:00
Optimization in Statistics and Data Science: Some Perspectives Past and Present
17:45:00
Adjourn for the Day
Saturday, November 16, 2019
08:30:00–09:00:00
Breakfast
Session 5: Integer Programming, Econometrics and Statistics
09:00:00–09:45:00
Machine Learning Under a Modern Optimization Lens
09:45:00–10:30:00
Structured Statistical Learning at Scale: Convex and Mixed Integer Optimization Perspectives
10:30:00–10:45:00
Break
10:45:00–11:30:00
Advances at the Intersection of Integer Programming, Data Science, and Econometrics
11:30:00–12:15:00
Building Representative Matched Samples with Multi-valued Treatments in Large Observational Studies
12:15:00–13:00:00
Lunch
Session 6: Linear Programming and Econometrics
13:00:00–13:45:00
Vector Quantile Regression: Quantile Regression Meets Optimal Transport
13:45:00–14:30:00
Differentiable Ranks and Quantiles Using Optimal Transport
14:30:00–15:15:00
Nonparametric Estimates of Demand in the California Health Insurance Exchange
15:15:00–15:30:00
Linear Programming Aspects of Regression Rankscore Inference
15:30:00–15:45:00
Break
Session 7: Stochastic Programming and Econometrics
15:45:00–16:30:00
Inference by Stochastic Optimization: A Free Lunch Bootstrap
16:30:00–17:15:00
Spatial Price Integration in Competitive Markets with Capacitated Transportation Networks
17:15:00–17:20:00