Interesting Papers of the Moment

Missing Events in Event Studies: Identifying the Effects of Partially-Measured News Surprises
by Refet S. Guerkaynak, Burcin Kisacikoglu, Jonathan H. Wright #25016 (AP ME)
http://papers.nber.org/papers/w25016?utm_campaign=ntw&utm_medium=email&utm_source=ntw

Colacito, Ric, Bridget Hoffmann, and Toan Phan (2018) “Temperature and growth: A panel
analysis of the United States,”
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2546456

Do You Know That I Know That You Know...? Higher-Order Beliefs in Survey Data
by Olivier Coibion, Yuriy Gorodnichenko, Saten Kumar, Jane Ryngaert #24987 (EFG ME)
http://papers.nber.org/papers/w24987?utm_campaign=ntw&utm_medium=email&utm_source=ntw

Economics Working Papers now in arXiv

Economics working papers are now a part of arXiv.  This is great news, as arXiv is the premier working paper hosting platform in mathematics and the mathematical / statistical sciences.  The Economics arXiv will start with a single subject area of Econometrics (econ.EM).  More economics subject areas will be added (of course), and moreover, subject areas can and will be subdivided.  Hats off to the econ.EM team (Victor Chernozhukov, MIT; Iván Fernández-Val, Boston University; Marc Henry, Penn State; Francesca Molinari, Cornell; Jörg Stoye, Bonn & Cornell; Martin Weidner, University College London).  The full announcement is here.

More on Nonlinear Forecasting Over the Cycle

Related to my last post, here's a new paper that just arrived from Rachidi Kotchoni and Dalibor Stevanovic, "Forecasting U.S. Recessions and Economic Activity". It's not non-parametric, but it is non-linear. As Dalibor put it, "The method is very simple: predict turning points and recession probabilities in the first step, and then augment a direct AR model with the forecasted probability." Kotchoni-Stevanovic and Guerron-Quintana-Zhong are usefully read together.

Nearest-Neighbor Forecasting in Times of Crisis

Nonparametric K-nearest-neighbor forecasting remains natural and obvious and potentially very useful, as it has been since its inception long ago.

[Most crudely: Find the K-history closest to the present K-history, see what followed it, and use that as a forecast. Slightly less crudely: Find the N K-histories closest to the present K-history, see what followed each of them, and take an average. There are many obvious additional refinements.]

Overall, nearest-neighbor forecasting remains curiously under-utilized in dynamic econometrics. Maybe that will change. In an interesting recent development, for example, new Federal Reserve System research by Pablo Guerron-Quintana and Molin Zhong puts nearest-neighbor methods to good use for forecasting in times of crisis.

An important Example of Simultaneously Wide and Dense Data

By the way, related to my last post on wide and dense data, an important example of analysis of data that are both wide and dense is the high-frequency high-dimensional factor modeling of Pelger and Ait-Sahalia and Xiu.  Effectively they treat wide sets of realized volatilities, each of which is constructed from underlying dense data.

Dual Regression

Speaking of quantiles and quantile regression, I also like the new version of Spady and Stouli's "Dual Regression." The power and insights of quantile regression, without the possibility of intersecting conditional quantile surfaces.  Sounds good to me.