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.

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."

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.

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