Tuesday, February 19, 2019

Berk-Nash Equilibrium and Pseudo MLE

The Berk statistics/econometrics tradition is alive and well, appearing now as Berk-Nash equilibrium in cutting-edge economic theory.  See for example Kevin He's Harvard job-market paper here and the references therein, and the slides from yesterday's lunch talk by my Penn colleague Yuichi Yamamoto.  But the connection between Berk-Nash equilibrium of economic theory and KLIC-minimizing Gaussian pseudo-MLE of econometric theory is under-developed. When the Berk-Nash people get better acquainted with Berk-White people, good things may happen.  Effectively Yuichi is pushing in that direction, working toward characterizing log-run behavior of likelihood maximizers rather than beliefs.

Sunday, January 27, 2019

Mixed-Frequency Big Data

Of course I have blogged on this earlier, e.g. here and here, and I am a fan. The latest is Andreou, Gagliardini, Ghysels, and Rubin, "Inference in Group Factor Models with an Application to Mixed Frequency Data". The latest revision, available here, is now forthcoming in Econometrica.

Friday, January 25, 2019

Score-Driven and Nonlinear Time-Series Models

Check out the upcoming conference here.  Definitely worth reading through the program.  Earlier related post here.




Network Data and Machine Learning

This just arrived, announcing an upcoming conference on the ML/networks interface.  It's definitely worth reading through the synopsis and topics and titles and authors.

"An exciting workshop on Machine Learning for Network Data is taking place at New York University on January 29. The event will discuss emerging challenges on generalizing the successes of image and speech processing to information domains with irregular structure. The workshop includes highlight talks by Yann LeCun and Brian Sadler as well as short talks by a collection of national leaders in the development of machine learning techniques for processing network data. The event is free to attend and open to the public but registration is required because of space limitations. Please visit the workshop site to access the registration form."

Monday, January 21, 2019

Machine Learning for Economists

My Penn colleague Jesus Fernandez-Villaverde has a nice slide deck here.  He asked me to warn you that this is a highly-preliminary version (0.1!), and to thank, without implicating, Stephen Hansen, as the deck draws on joint work.

Monday, January 7, 2019

Papers of the Moment

Happy New Year!

I was surprised at the interest generated when I last listed a few new intriguing working papers that I'm reading and enjoying.  Maybe another such posting is a good way to start the new year.  Hear are three:

Understanding Regressions with Observations Collected at High Frequency over Long Span

Chang, Yoosoon; Lu, Ye; Park, Joon Y.

Abstract:
In this paper, we analyze regressions with observations collected at small time interval over long period of time. For the formal asymptotic analysis, we assume that samples are obtained from continuous time stochastic processes, and let the sampling interval δ shrink down to zero and the sample span T increase up to infinity. In this setup, we show that the standard Wald statistic diverges to infinity and the regression becomes spurious as long as δ → 0 sufficiently fast relative to T → ∞. Such a phenomenon is indeed what is frequently observed in practice for the type of regressions considered in the paper. In contrast, our asymptotic theory predicts that the spuriousness disappears if we use the robust version of the Wald test with an appropriate longrun variance estimate. This is supported, strongly and unambiguously, by our empirical illustration.

http://d.repec.org/n?u=RePEc:syd:wpaper:2018-10&r=ets

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Equity Concerns are Narrowly Framed

Christine L. Exley and Judd B. Kessler

Abstract:
We show that individuals narrowly bracket their equity concerns. Across four experiments including 1,600 subjects, individuals equalize components of payoffs rather than overall payoffs. When earnings are comprised of "small tokens" worth 1 cent and "large tokens" worth 2 cents, subjects frequently equalize the distribution of small (or large) tokens rather than equalizing total earnings. When payoffs are comprised of time and money, subjects similarly equalize the distribution of time (or money) rather than total payoffs. In addition, subjects are more likely to equalize time than money. These findings can help explain a variety of behavioral phenomena including the structure of social insurance programs, patterns of public good provision, and why transactions that turn money into time are often deemed repugnant. 

https://www.nber.org/papers/w25326?utm_campaign=ntwh&utm_medium=email&utm_source=ntwg9

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Shackling the Identification Police?

Christopher J. Ruhm

Abstract:
This paper examines potential tradeoffs between research methods in answering important questions versus providing more cleanly identified estimates on problems that are potentially of lesser interest. The strengths and limitations of experimental and quasi-experimental methods are discussed and it is postulated that confidence in the results obtained may sometimes be overvalued compared to the importance of the topics addressed. The consequences of this are modeled and several suggestions are provided regarding possible steps to encourage greater focus on questions of fundamental importance. 

http://papers.nber.org/tmp/51337-w25320.pdf

Friday, December 21, 2018

Holiday Haze

File:Happy Holidays (5318408861).jpg
Happy holidays! 

Your blogger is about to vanish, returning in the new year. Many thanks for your past, present, and future support. 

If you're at ASSA Atlanta, I hope you'll come to the Penn Economics and Finance parties.

Sunday, December 16, 2018

Causality as Robust Prediction

I like thinking about causal estimation as a type of prediction (e.g., here). Here's a very nice slide deck from Peter Buhlmann at ETH Zurich detailing his group's recent and ongoing work in that tradition.














Thursday, December 13, 2018

More on Google Dataset Search

Some months ago I blogged on Google's new development of a dataset search tool.  Evidently it's coming along.  Check out the beta version here. Also, on dataset supply as opposed to demand, see here for how to maximize visibility of your datasets to the search engine.

[With thanks to the IIF's Oracle newsletter for alerting me.]


Monday, December 10, 2018

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