Best Webinar Awards, III (IAAE)

Now let's do the always-stimulating International Association for Applied Econometrics webinar. The winner is:

Andrii Babii (UNC Chapel Hill), 

for 

"Binary Choice with Asymmetric Loss and Fairness in

Machine Learning Classification, with an Application to Racial Justice,"

with Chen, Ghysels, and Kumar. Check out the paper here, and video+slides here.

Asymmetric loss is crucially relevant in some situations; consider, for example, classification as "guilty" or "non guilty". Traditional classification methods have a hard time with it, however, as they ultimately treat type I and II errors symmetrically. (See, e.g., here.) This paper makes impressive progress.

Abstract:

The importance of asymmetries in prediction problems arising in economics has been recognized for a long time. In this paper, we focus on binary choice problems in a data-rich environment with general loss functions. In contrast to the asymmetric regression problems, the binary choice with general loss functions and high-dimensional datasets is challenging and not well understood. Econometricians have studied binary choice problems for a long time, but the literature does not offer computationally attractive solutions in data-rich environments. In contrast, the machine learning literature has many computationally attractive algorithms that form the basis for much of the automated procedures that are implemented in practice, but it is focused on symmetric loss functions that are independent of individual characteristics. One of the main contributions of our paper is to show that the theoretically valid predictions of binary outcomes with arbitrary loss functions can be achieved via a very simple reweighting of the logistic regression, or other state-of-the-art machine learning techniques, such as boosting or (deep) neural networks. We apply our analysis to racial justice in pretrial detention.

See also here.

Best Webinar Awards, II (Chamberlain)

Now let's do the wonderful and pioneering Chamberlain Seminar.

The winner is:

Elena Manresa (NYU), 

for 

"An Adversarial Approach to Structural Estimation,"

with Tetsuya Kaji and Guillaume Pouliot! Check out the paper and video+slides.

It knocked me off my feet (and a few others – there were 900+ viewers). The way I see it -- although the approach is actually much more sophisticated than the description I'm about to give -- she proposes and explores, theoretically and empirically, the use of machine learning (ML) approximators like neural nets (NNs), random forests, etc. as windows for indirect inference in structural econometric models. This is a big deal, as ML approximators are potentially very sophisticated tools for characterizing model and data properties, thereby sharpening our ability to detect divergences between them. Of course her paper raises many questions as well, as does all good research, for example whether the numerous local optima associated with NNs will complicate the resulting indirect inference estimation. In any event the work is tremendously stimulating – a long and exciting way from casual GMM based on a few moments selected in ad hoc fashion, and a very nice bridge between the econometrics and data science / ML literatures. The paper was a real “eureka moment” for me. 

Abstract:

We propose a new simulation-based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates synthetic observations using the structural model) and a discriminator (which classifies if an observation is synthetic). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency under correct specification and the parametric rate under misspecification. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence. We apply our method to the elderly’s saving decision model and show that including gender and health profiles in the discriminator uncovers the bequest motive as an important source of saving across the wealth distribution, not only for the rich.

2020 Best Webinar Awards, I (FRBSF)

I'm sure you've been anxiously awaiting my (first annual?) "Best of 2020" econometrics retrospective! Let's do "best new webinars".  I'll list my top webinars in this and forthcoming posts (in no particular order) and select a "best talk" from each.  Of course they're filled with great talks -- that's why they're my favorite webinars -- quite apart from my personal selection for best talk.

Let's start with the Federal Reserve Bank of San Francisco's rock-solid Virtual Seminar on Climate Economics. 

And the winner is:

Solomon Hsiang (Berkeley), 

for 

"Valuing the Global Mortality Consequences of Climate Change"!

Congrats to Sol and his 16 coauthors (yes, 16!) for producing a truly breathtaking global empirical analysis, blending massive observational data and climate model simulations to help inform a pressing issue of global importance.  Check out the paper and video. 

ABSTRACT This paper develops the first globally comprehensive and empirically grounded estimates of mortality risk due to future temperature increases caused by climate change. Using 40 countries' subnational data, we estimate age-specific mortality-temperature relationships that enable both extrapolation to countries without data and projection into future years while accounting for adaptation. We uncover a U-shaped relationship where extreme cold and hot temperatures increase mortality rates, especially for the elderly, that is flattened by both higher incomes and adaptation to local climate (e.g., robust heating systems in cold climates and cooling systems in hot climates). Further, we develop a revealed preference approach to recover unobserved adaptation costs. We combine these components with 33 high-resolution climate simulations that together capture scientific uncertainty about the degree of future temperature change. Under a high emissions scenario, we estimate the mean increase in mortality risk is valued at roughly 3.2% of global GDP in 2100, with today's cold locations benefiting and damages being especially large in today's poor and/or hot locations. Finally, we estimate that the release of an additional ton of CO2 today will cause mean [interquartile range] damages of $36.6 [-$7.8, $73.0] under a high emissions scenario and $17.1 [-$24.7, $53.6] under a moderate scenario, using a 2% discount rate that is justified by US Treasury rates over the last two decades. Globally, these empirically grounded estimates substantially exceed the previous literature's estimates that lacked similar empirical grounding, suggesting that revision of the estimated economic damage from climate change is warranted.

Polarized Pandemic Response Across U.S. States

Umut Akovali and Kamil Yilmaz have an insightful new VOX EU piece on the polarized pandemic response and Covid-19 connectedness across U.S. states.  Here's a teaser:

International Association of Applied Econometrics 2020 Fellows

Here is the class of 2020. What a stellar group! (My reaction to almost every new fellow is : How could s/he not ALREADY be a fellow?) For more IAAE info (webinars, conferences, etc.) check https://appliedeconometrics.org/.

Alberto Abadie (Massachusetts Institute of Technology)
Yacine Ait-Sahalia (Princeton University)
Torben G Andersen (Northwestern University)
Peter Arcidiacono (Duke University
Orazio Attanasio (Yale University)
Christiane Baumeister (Notre Dame University)
Hilde Bjornland (B.I. Norwegian Business School)
Moshe Buchinsky (University of California Los Angeles)
Monica Costa Dias (Institute for Fiscal Studies)
Aureo de Paula (University College London)
Ana Beatriz Galvao (University of Warwick)
Eric M. Ghysels (University of North Carolina at Chapel Hill)
Kei Hirano (Penn State University)
Han Hong (Stanford University)
V. Joseph Hotz (Duke University)
Oscar Jorda (University of California Davis)
Chang-Jin Kim (University of Washington)
Roger Koenker (University of Illinois)
Gary Koop (University of Strathclyde)
Guido M. Kuersteiner (University of Maryland)
Simon Lee (Columbia University)
Arthur Lewbel (Boston College)
Tong Li (Vanderbilt University)
Michael McCracken (Federal Reserve Bank of Saint Louis)
Anna Mikusheva (Massachusetts Institute of Technology)
Marcelo Moreira (Fundação Getulio Vargas )
Ulrich Mueller (Princeton University)
Charles Nelson (University of Washington)
Andriy Norets (Brown University)
Denise Osborn (University of Manchester)
Harry Paarsch (University of Central Florida)
Franco Peracchi (Georgetown University)
Jack Porter (University of Wisconsin)
Eric Renault (University of Warwick)
Joseph Romano (Stanford University)
Olivier Scaillet (Université de Genève)
Susanne Schennach (Brown University)
Enrique Sentana (CEMFI)
Matthew Shum (California Institute of Technology)
Christopher Sims (Princeton University)
Ron Smith (Birkbeck, University of London
Joerg Stoye (Cornell University)
Justin Tobias (Purdue University)
Pravin K Trivedi (University of Queensland / Indiana University)
Aman Ullah (University of California Riverside)
Quang Vuong (New York University)
Frank Windmeijer (University of Oxford)
Frank Wolak (Stanford University)

Climate Science Meets Indirect Inference

Greetings from the American Geophysical Union annual meeting, virtually of course. Climate science is starting to use machine learning (ML) to find good auxiliary models for indirect inference estimation of structural climate models. (Never mind that climate science has never heard of indirect inference!) The use of ML to obtain sophisticated auxiliary models parallels the recent beautiful structural econometric work of Kaji, Manresa, and Pouliot.  A nice Manresa seminar video with discussion is here.

Guide to Discrete-Time Yield Curve Modelling

Ken Nyholm's book is finally out from Cambridge U Press. It's a fine introduction, with MATLAB code.  Great for students.

Best of all, it's FREE for download until December 18!   https://doi.org/10.1017/9781108975537

The code is at https://ken-nyholm.com/papers and remains free forever.

The Great Recession Appears so Quaint

 

ADS U.S. real economic activity index then and now...

2020 EC2 Program Now Posted

Looking great!

31th (EC)^2 Conference: High Dimensional Modeling in Time Series

December 11-12, 2020

Paris, France (Alas, virtually...)

Program, registration, etc. at http://ec2.essec.edu/

Classification Under Asymmetric Loss

I just read the stimulating new paper by Babii et al. on binary choice / classification w asymmetric loss, https://arxiv.org/abs/2010.08463.

It led me to recall some work of mine with Peter Christoffersen that may be related in interesting ways. The hyperlinked papers are below. We study optimal prediction under asy loss, focusing not only on how the amount of loss asymmetry drives the optimal bias (of course, as in Granger's seminal work), but also focusing on how heteroskedasticity​ (H), interacting with loss asymmetry, drives the optimal bias.  (The optimal bias increases as variance increases, and conversely.)  

We focus on time-series H, but of course cross section H is massively relevant as well, so I wonder how it would all work out in theory and practice in the Babii et al. cross-section classification environment.  Of course everyone talks about H destroying consistency in logit and related models, but that's deeper econometric consistency for marginal effects etc. I don't see why it would destroy consistency for the optimal prediction / classification, which is automatically induced by virtue of the estimation criterion as routinely exploited in the ML literature.

In any event the key recognition is that heteroskedasticity and asymmetric loss interact. Asymmetric loss of course influences the optimal prediction / classification, but it influences it more in regions (cross section) or periods (time series) where / when variance is high.

Christoffersen, P. and Diebold, F.X. (1997), "Optimal Prediction Under Asymmetric Loss," Econometric Theory, 13, 808-817.

Christoffersen, P.F. and Diebold, F.X. (1996), "Further Results on Forecasting and Model Selection Under Asymmetric Loss," Journal of Applied Econometrics, 11, 561-571.

(Somewhat) related earlier No Hesitations post:
https://fxdiebold.blogspot.com/search/label/Classification