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 info on how to maximize visibility (of your datasets...) to the Google dataset 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

Monday, December 3, 2018

Dual Regression and Prediction

Richard Spady and Sami Stouli have an interesting new paper, “Dual Regression". They change the usual OLS loss function from quadratic to something related but different, as per their equation (2.2), and they get impressive properties for estimation under correct specification. They also have results under misspecification.

I'd like to understand more regarding dual regression's properties for prediction under misspecification. Generally we're comfortable with quadratic loss, in which case OLS delivers the goods (the conditional mean or linear projection) in large samples under great generality (e.g., see here). The dual regression estimator, in contrast, has a different probability limit under misspecification -- it's not providing a KLIC-optimal approximation.

If the above sounds negative, note well that the issue raised may be an opportunity, not a pitfall! Certainly there is nothing sacred about quadratic loss, even if the conditional mean is usually a natural predictor. We sometimes move to absolute-error loss (conditional median predictor), check-function loss (conditional quantile predictor), or all sorts of other predictive loss functions depending on the situation. But movements away from conditional mean or median prediction generally require some justification and interpretation. Equivalently, movements away from quadratic or absolute predictive loss generally require some justification and interpretation. I look forward to seeing that for the loss function that drives dual regression.

Friday, November 16, 2018

Nearest-Neighbor Prediction

The beautiful idea has been around for ages. Find the N closest H-histories to the current H-history (you choose/tune N and H), for each H-history see what followed, take an average, and use that as your forecast. Of course there are many variations and extensions. Interesting new work by Dendramis, Kapetanios, and Marcellino is in exactly that tradition, except that Dendramis et al.  don't show much awareness of the tradition, or attempt to stand on its shoulders, which I find odd. I find myself hungry for tighter connections, for example to my favorite old nearest-neighbor prediction piece, Sid Yakowitz's well-known "Nearest-Neighbor Methods for Time Series Analysis,” Journal of Time Series Analysis, 1987.

Thursday, November 15, 2018

JFEC Special Issue for Peter Christoffersen

No, I have not gone into seclusion. Well actually I have, but not intentionally and certainly not for lack of interest in the blog. Just the usual crazy time of year, only worse this year for some reason. Anyway I'll be back very soon, with lots to say! But here's something important and timely, so it can't wait:

Journal of Financial Econometrics

Call for Papers

Special Issue in Honor of Peter Christoffersen

The Journal of Financial Econometrics is organizing a special issue in memory of Professor Peter
Christoffersen, our friend and colleague, who passed away in June 2018. Peter held the TMX Chair in Capital Markets and a Bank of Canada Fellowship and was a widely respected member of the Rotman School at the University of Toronto since 2010. Prior to 2010, Peter was a valued member of the Desautels Faculty of Management at McGill University. In addition to his transformative work in econometrics and volatility models, financial risk and financial innovation had been the focus of Peter’s work in recent years.

We invite paper submissions on topics related to Peter’s contributions to Finance and Econometrics. We are particularly interested in papers related to the following topics:

1)   The use of option-implied information for forecasting; Rare disasters and portfolio
management; Factor structures in derivatives and futures markets.

2)   Volatility, correlation, extreme events, systemic risk and Value-at-Risk modeling for
financial market risk management.

3)   The econometrics of digital assets; Big data and Machine Learning.

To submit a paper, authors should login to the Journal of Financial Econometrics online submission system and follow the submission instructions as per journal policy.  The due date for submissions is June 30, 2019.  It is important to specify in the cover letter that the paper is submitted to the special issue in honor of Peter Christoffersen, otherwise your paper will not be assigned to the guest editors.

Guest Editors

•    Francis X. Diebold, University of Pennsylvania

•    René Garcia, Université de Montréal and Toulouse School of Economics

•    Kris Jacobs, University of Houston

Monday, October 29, 2018

Becker Friedman Expectations Conference

I just returned from a great BFI Conference at U Chicago, Developing and Using Business Expectations Data, organized by Nick Bloom and Steve Davis.

Wonderfully, density as opposed to point survey forecasts were featured throughout. There was the latest on central bank surveys (e.g., Binder et al.), but most informative (to me) was the emphasis on surveys that I'm less familiar with, typically soliciting density expectations from hundreds or thousands of C-suite types at major firms. Examples include Germany's important IFO survey (e.g., Bachman et al.), the U.S. Census Management and Organizational Practices Survey (e.g., Bloom et al.)., and fascinating work in progress at FRB Atlanta. 

The Census survey is especially interesting due to its innovative structuring of histogram bins. There are no fixed bins. Instead users give 5 bins of their own choice, and five corresponding probabilities (which add to 1). This solves the problem in fixed-bin surveys of  (lazy? behaviorally-biased?) respondents routinely and repeatedly assigning 0 probability to subsequently-realized events.

Sunday, October 28, 2018

Expansions Don't Die of Old Age

As the expansion ages, there's progressively more discussion of whether its advanced age makes it more likely to end. The answer is no. More formally, postwar U.S. expansion hazards are basically flat, in contrast to contraction hazards, which are sharply increasing. Of course the present expansion will eventually end, and it may even end soon, but its age it unrelated to its probability of ending.

All of this is very clear in Diebold, Rudebusch and Sichel (1992). See Figure 6.2 on p. 271. (Sorry for the poor photocopy quality.) The flat expansion hazard result has held up well (e.g., Rudebusch (2016)), and moreover it would only be strengthened by the current long expansion.

[I blogged on flat expansion hazards before, but the message bears repeating as the expansion continues to age.]

Thursday, October 4, 2018

In Memoriam Herman Stekler

I am sad to report that Herman Stekler passed away last month. I didn't know until now. He was a very early and important and colorful -- indeed unique -- personage in the forecasting community, making especially noteworthy contributions to forecast evaluation.  
https://forecasters.org/herman-stekler_oracle-oct-2018/

Tuesday, October 2, 2018

Tyranny of the Top 5 Econ Journals

Check out:

PUBLISHING AND PROMOTION IN ECONOMICS: THE TYRANNY OF THE TOP FIVE 
by
James J. Heckman and Sidharth Moktan 
NBER Working Paper 25093
http://www.nber.org/papers/w25093

Heckman et al. examine a range of data from a variety of perspectives, analyze them thoroughly, and pull no punches in describing their striking results.

It's a great paper. There's a lot I could add, maybe in a future post, but my blood pressure is already high enough for today. So I'll just leave you with a few choice quotes from the paper ["T5" means "top-5 economics journals" ]:

"The results ... support the hypothesis that the T5 influence operates through channels that are independent of article quality."

"Reliance on the T5 to screen talent incentivizes careerism over creativity."

"Economists at highly ranked departments with established reputations are increasingly not publishing in T5 or field journals and more often post papers online in influential working paper series, which are highly cited, but not counted as T5s."

"Many non-T5 articles are better cited than many articles in T5 journals. ...  Indeed, many of the most important papers published in the past 50 years have been too innovative to survive the T5 gauntlet."

"The [list of] most cited non-T5 papers reads like an honor roll of economic analysis."

"The T5 ignores publication of books. Becker’s Human Capital
(1964) has more than 4 times the number of citations of any paper listed on RePEc. The exclusion of books from citation warps incentives against broad and integrated research and towards writing bite-sized fragments of ideas."

Saturday, September 29, 2018

RCT's vs. RDD's

Art Owen and Hal Varian have an eye-opening new paper, "Optimizing the Tie-Breaker Regression Discontinuity Design".

Randomized controlled trials (RCT's) are clearly the gold standard in terms of statistical efficiency for teasing out causal effects. Assume that you really can do an RCT. Why then would you ever want to do anything else?

Answer: There may be important considerations beyond statistical efficiency. Take the famous "scholarship example". (You want to know whether receipt of an academic scholarship causes enhanced academic performance among strong scholarship test performers.) In an RCT approach you're going to give lots of academic scholarships to lots of randomly-selected people, many of whom are not strong performers. That's wasteful. In a regression discontinuity design (RDD) approach ("give scholarships only to strong performers who score above X in the scholarship exam, and compare the performances of students who scored just above and below X"), you don't give any scholarships to weak performers. So it's not wasteful -- but the resulting inference is statistically inefficient. 

"Tie breakers" implement a middle ground: Definitely don't give scholarships to bottom performers, definitely do give scholarships to top performers, and randomize for a middle group. So you gain some efficiency relative to pure RDD (but you're a little wasteful), and you're less wasteful than a pure RCT (but you lose some efficiency).

Hence there's an trade-off, and your location on it depends on the size of the your middle group. Owen and Varian characterize the trade-off and show how to optimize the size of the middle group. Really nice, clean, and useful.

[Sorry but I'm running way behind. I saw Hal present this work a few months ago at a fine ECB meeting on predictive modeling.]