Greater New York Area Econometrics Colloquium

Last week's 13th annual Greater New York Area Econometrics Colloquium, generously hosted by Princeton, was a great success, with strong papers throughout. The program is below. I found two papers especially interesting. I already blogged on Spady and Stouli's “Simultaneous Mean-Variance Regression”. The other was "Nonparametric Sample Splitting", by Lee and Wang.

Think of a nonlinear classification problem. In general the decision boundary is of course a highly nonlinear surface, but it's a supervised learning situation, so it's "easy" to learn the surface using standard nonlinear regression methods. Lee and Wang, in contrast, study an unsupervised learning situation, effectively a threshold regression model, where the threshold is determined by an unknown nonparametric relation. And they have very cool applications to things like estimating effective economic borders, gerrymandering, etc. 

The 13th Greater New York Metropolitan Area Econometrics Colloquium

Princeton University, Saturday, December 1, 2018

9.00am-10.30am: Session 1

“Simple Inference for Projections and Linear Programs” by Hiroaki Kaido (BU), Francesca Molinari (Cornell), and Jörg Stoye (Cornell)

“Clustering for multi-dimensional heterogeneity with application to production function estimation” by Xu Cheng (UPenn), Peng Shao (UPenn), and Frank Schorfheide (UPenn)

“Adaptive Bayesian Estimation of Mixed Discrete-Continuous Distributions Under Smoothness and Sparsity” by Andriy Norets (Brown) and Justinas Pelenis (Vienna IAS)

11.00am-12.30pm: Session 2

“Factor-Driven Two-Regime Regression” by Sokbae Lee (Columbia), Yuan Liao (Rutgers), Myung Hwan Seo (Cowles), and Youngki Shin (McMaster)

“Semiparametric Estimation in Continuous-Time: Asymptotics for Integrated Volatility Functionals with Small and Large Bandwidths” by Xiye Yang (Rutgers)

“Nonparametric Sample Splitting” by Yoonseok Lee (Syracuse) and Yulong Wang (Syracuse)

2.00pm-3.30pm: Session 3

“Counterfactual Sensitivity and Robustness” by Timothy Christensen (NYU) and Benjamin Connault (IEX Group)

“Dynamically Optimal Treatment Allocation Using Reinforcement Learning” by Karun Adusumilli (UPenn), Friedrich Geiecke (LSE), and Claudio Schilter (LSE)

“Simultaneous Mean-Variance Regression” by Richard Spady (Johns Hopkins) and Sami Stouli (Bristol)

4.00pm-5.30pm: Session 4

“Semi-parametric instrument-free demand estimation: relaxing optimality and equilibrium assumptions” by Sungjin Cho (Seoul National), Gong Lee (Georgetown), John Rust (Georgetown), and Mengkai Yu (Georgetown)

“Nonparametric analysis of monotone choice” by Natalia Lazzati (UCSC), John Quah (Johns Hopkins), and Koji Shirai (Kwansei Gakuin)

“Discrete Choice under Risk with Limited Consideration” by Levon Barseghyan (Cornell), Francesca Molinari (Cornell), and Matthew Thirkettle (Cornell)

Organizing Committee

Bo Honoré, Michal Kolesár, Ulrich Müller, and Mikkel Plagborg-Møller

Participants Adusumilli  Karun UPenn
Althoff	Lukas	Princeton
Anderson	Rachel	Princeton
Bai	Jushan	Columbia
Beresteanu	Arie	Pitt
Callaway	Brantly	Temple
Chao	John	Maryland
Cheng	Xu	UPenn
Choi	Jungjun	Rutgers
Choi	Sung Hoon	Rutgers
Cox	Gregory	Columbia
Christensen	Timothy	NYU
Diebold	Frank	UPenn
Dou	Liyu	Princeton
Gao	Wayne	Yale
Gaurav	Abhishek	Princeton
Henry	Marc	Penn State
Ho	Paul	Princeton
Honoré	Bo	Princeton
Hu	Yingyao	Johns Hopkins
Kolesar	Michal	Princeton
Lazzati	Natalia	UCSC
Lee	Simon	Columbia
Li	Dake	Princeton
Li	Lixiong	Penn State
Liao	Yuan	Rutgers
Menzel	Konrad	NYU
Molinari	Francesca	Cornell
Montiel Olea	José Luis	Columbia
Müller	Ulrich	Princeton
Norets	Andriy	Brown
Plagborg-Møller	Mikkel	Princeton
Poirier	Alexandre	Georgetown
Quah	John	Johns Hopkins
Rust	John	Georgetown
Schorfheide	Frank	UPenn
Seo	Myung	SNU & Cowles
Shin	Youngki	McMaster
Sims	Christopher	Princeton
Spady	Richard	Johns Hopkins
Stoye	Jörg	Cornell
Taylor	Larry	Lehigh
Vinod	Hrishikesh	Fordham
Wang	Yulong	Syracuse
Yang	Xiye	Rutgers
Zeleneev	Andrei	Princeton