Until Next Year

Your blogger will be back in the New Year. 

Meanwhile, Happy Holidays to all!

Best Webinar Awards, V (Newcomers and Recap)

Two more exciting econometrics webinars have recently burst on the scene:

Climate Econometrics:  Just what it sounds like -- the interface of climate science and econometrics

AMLEDS: "Applied Machine Learning, Economics, and Data Science".  So far mostly the interface of machine learning and econometrics.

Too soon to make any awards, but stay tuned for next year!

-- 

To recap, the last few posts have featured, in no particular order:

Chamberlain Seminar

Society for Financial Econometrics online seminar

International Association for Applied Econometrics webinar

FRBSF Virtual Seminar on Climate Economics

Climate Econometrics

AMLEDS

What have I missed?

Best Webinar Awards, IV (SoFiE)

Now let's do the wonderful Society for Financial Econometrics online seminar.  It's a tie!  

The first winner is 

Rob Engle (NYU Stern), 

for 

"Measuring and Hedging Geopolitical Risk,"

with S. Martins.  A fascinating "geopolitical risk" interpretation of the factor structure in "idiosyncratic" components of asset return volatilities.

Abstract:  Geopolitical events can impact volatilities of all assets, asset classes, sectors and countries. It is shown that innovations to volatilities are correlated across assets and therefore can be used to measure and hedge geopolitical risk. We introduce a definition of geopolitical risk which is based on volatility shocks to a wide range of financial market prices. To measure geopolitical risk, we propose a statistical model for the magnitude of the common volatility shocks. Accordingly, a test and estimation methods are developed and studied using both empirical and simulated data. We provide a novel explanation for why idiosyncratic volatilities comove based on a new way to formulate multiplicative factors. Finally, we propose a new criterion for portfolio optimality which is intended to reduce the exposure to geopolitical risk.

The second winner is 

Patrick Gagliardini (U Lugano), 

for 

"Extracting Statistical Factors When Betas Are Time-Varying,"

with H. Ma.  Time-varying betas are a key route to time-varying financial network connectedness, near and dear to my heart.

Check out the videos here. 

Abstract: This paper deals with identification and inference on the unobservable conditional factor space and its dimension in large unbalanced panels of asset returns. The model specification is nonparametric regarding the way the loadings vary in time as functions of common shocks and individual characteristics. The number of active factors can also be time-varying as an effect of the changing macroeconomic environment. The method deploys Instrumental Variables (IV) which have full-rank covariation with the factor betas in the cross-section. It allows for a large dimension of the vector generating the conditioning information by machine learning techniques. In an empirical application, we infer the conditional factor space in the panel of monthly returns of individual stocks in the CRSP dataset between January 1971 and December 2017.

Climate Finance

Very nice empirically-oriented (and empirically-sophisticated) Giglio-Kelly-Stroebel survey of climate change and financial markets here.  Ungated copy here.   

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

Essie Maasoumi Econometric Theory Interview

Check it out here. So fine and so appropriate for Essie.

More generally, seeing the latest reminds me of the invaluable ET Interviews series. Piece-by-piece, thanks to the initiative of Peter Phillips at ET, it is assembling a history of modern econometric thought. May its future be as vibrant as its past!

See here for some background circa 2015.

Russian Holidays Predict Troll Activity 2015-2017

A fascinating new abstract. Timely too. 

Russian Holidays Predict Troll Activity 2015-2017 Douglas Almond, Xinming Du, and Alana Vogel #28035 Abstract: While international election interference is not new, Russia is credited with “industrializing” trolling on English-language social media platforms. In October 2018, Twitter retrospectively identified 2.9 million English-language tweets as covertly written by trolls from Russia's Internet Research Agency. Most active 2015-2017, these Russian trolls generally supported the Trump campaign (Senate Intelligence Committee, 2019) and researchers have traced how this content disseminated across Twitter. Here, we take a different tack and seek exogenous drivers of Russian troll activity. We find that trolling fell 35% on Russian holidays and to a lesser extent, when temperatures were cold in St. Petersburg. More recent trolls released by Twitter do not show any systematic relationship to holidays and temperature, although substantially fewer of these that have been made public to date. Our finding for the pre-2018 interference period may furnish a natural experiment for evaluati! ng the causal effect of Russian trolling on indirectly-affected outcomes and political behaviors — outcomes that are less traceable to troll content and potentially more important to policymakers than the direct dissemination activities previously studied. As a case in point, we describe suggestive evidence that Russian holidays impacted daily trading prices in 2016 election betting markets.

Econometrics / Machine Learning Interface

Check out this exciting new seminar series. Great initiative!

AMLEDS Seminar (Applied Machine Learning, Economics, and Data Science)

Serena Ng (Columbia University) will give the inaugural seminar. The discussion moderator is Michael McMahon (Oxford University).

Topic: "Methods for Analyzing Data with Missing Values and from New Sources"

13:00 EST time / 18:00 GMT time / 19:00 CET time, November 6, 2020. 

To register, and to learn more about AMLEDS, go to https://sites.google.com/view/amleds/home.

Factor Loadings and Network Connectedness

I have long been interested in using time-varying latent factor loadings for time-varying connectedness measurement. Patrick Gagliardini made great progress on time-varying loadings in his October 19 SoFiE talk, discussed by Seth Pruitt (recording etc, here).  

I want to relate time-varying latent factor loadings to the Diebold-Yilmaz connectedness measurement framework. Kelly et al. (2019) helps, showing how to empirically assess the correlation, if any, between time-varying factor loadings and time-varying DY network connectedness, by allowing loadings to depend on covariates like connectedness.

But what I really want is a "Rosetta Stone" giving a 1-1 translation between Patrick's "time-varying factor loadings" world and the DY "time varying network centrality" world. That's almost surely wishful thinking in general, but maybe under some (stringent) conditions?

PREDOC

Check out www.predoc.org.  It just launched.  Moving forward I think this will be a key clearinghouse for information for diverse undergrads seeking an economics "pre-doc" before potentially heading on for the Ph.D. Current members here, and growing daily, as are available predoc positions.

H. O. Stekler Research Program on Forecasting

November 5, 2020, 12:30 pm - 2 pm ET (Virtual): Launch event for the H. O. Stekler Research Program on Forecasting at George Washington University. Panelists will include Neil Ericsson, Fred Joutz, Prakash Loungani, and Tara Sinclair. Please email forecasting@gwu.edu to register (no cost) and receive a Zoom link to join the meeting online. 

60-Second Lecture

I hope you can join us...

60-Second Lectures | September 30, 2020

60-Second Lectures

Watch on Twitter and Facebook (@pennsas)

Wednesday, September 30, 2020 - Noon, Philadelphia time

Every spring and fall, Penn Arts & Sciences faculty take a minute to share their perspectives on a variety of topics. The theme for our talks this semester is “Social Institutions During Social Distancing.”

Social isolation, economic hardship, and questioning of our government and collective response to the pandemic has combined with reinvigorated demands for racial justice during these challenging times. These circumstances have led many of us to think more deeply about the glue that holds us together as a society. In this series we’ve asked faculty to share their observations on our social institutions, the role they play, and whether they’re working.

Faculty Speaker:
Entering the Pandemic: The Joint Progression of COVID-19 and Economic Growth in the U.S.
Francis Diebold, Paul F. and Warren Shafer Miller Professor Social Sciences and Professor of Economics, Finance, and Statistics

New Econometric Society Fellows

What a wonderful new crop for 2020.  Special congratulations to my Penn colleagues Dirk Krueger and Jesús Fernández-Villaverde!

September 22, 2020

www.econometricsociety.org

The Society is pleased to announce the election of 46 new Fellows of the Econometric Society. Manuel Amador, University of Minnesota Isaiah Andrews, Harvard University Raouf Boucekkine, Aix-Marseille Université Moshe Buchinsky, University of California, Los Angeles Aureo de Paula, University College London Melissa Dell, Harvard University Peter DeMarzo, Stanford University Habiba Djebbari, Aix-Marseille Université Matthias Doepke, Northwestern University Federico Echenique, California Institute of Technology Chris Edmond, University of Melbourne Joan María Esteban, Barcelona GSE Jesús Fernández-Villaverde, University of Pennsylvania Christopher J. Flinn, New York University Nicola Fuchs-Schündeln, Goethe University Frankfurt Alfred Galichon, New York University Paris Pierre-Olivier Gourinchas, University of California, Berkeley Kaddour Hadri, Queen’s University Belfast Marina Halac, Yale University Charles I. Jones, Stanford University Emir Kamenica, University of Chicago Greg Kaplan, University of Chicago Maxwell King, Monash University Dirk Krueger, University of Pennsylvania Gilat Levy, London School of Economics Francesca Molinari, Cornell University Massimo Morelli, Bocconi University Jessica Pan, National University of Singapore Alessandro Pavan, Northwestern University Thomas Philippon, New York University John K.H. Quah, Johns Hopkins University and National University of Singapore Imran Rasul, University College London Stephen J. Redding, Princeton University Ernesto Schargrodsky, Universidad Torcuato Di Tella Martin Schneider, Stanford University Carl Shapiro, University of California, Berkeley Margaret Slade, University of British Columbia Rodrigo Soares, Columbia University Chad Syverson, University of Chicago Adam Szeidl, Central European University Steve Tadelis, University of California, Berkeley Satoru Takahashi, National University of Singapore Fernando Vega-Redondo, Bocconi University Heidi Williams, Stanford University Steven R. Williams, University of Melbourne Muhamet Yildiz, Massachusetts Institute of Technology The Society is grateful for the work of its 2020 Fellows Nominating Committee (Liran Einav (Chair), Daron Acemoglu, Martin Cripps, Gabrielle Demange, Ignacio Lobato, Rosa Matzkin, and Hélène Rey) and for all the nominations initiated by its members.

 

Climate Week at Penn

Check out Climate Week at Penn. My part is the Energy Economics and Finance seminar in the Kleinman Center for Energy Policy, Wednesday 9/23, on Arctic Sea Ice. The direct link is here. Delighted to be a part of both the seminar and Climate Week -- what a nice combination!

Lawrence R. Klein at 100

On Monday I'm speaking at Lawrence R. Klein's 100th birthday conference, generously hosted by the University of Costa Rica.  (Alas, only virtually via Zoom, and of course without Larry, who departed in 2013.)  My gratitude and admiration continue to grow.

Here's a lightly-edited update of some 2014 memorial remarks of mine:

I owe an immense debt of gratitude to Larry Klein, who helped guide, support, and inspire my career for more than three decades. Let me offer just a few vignettes.

Circa 1979 I was an undergraduate studying finance and economics at Penn's Wharton School, where I had my first economics job. I was as a research assistant at Larry's firm, Wharton Econometric Forecasting Associates (WEFA). I didn't know Larry at the time; I got the job via a professor whose course I had taken, who was a friend of a friend of Larry's. I worked for a year or so, perhaps ten or fifteen hours per week, on regional electricity demand modeling and forecasting. Down the hall were the U.S. quarterly and annual modeling groups, where I eventually moved and spent another year. Lots of fascinating people roamed the maze of cubicles, from eccentric genius-at-large Mike McCarthy, to Larry and Sonia Klein themselves, widely revered within WEFA as god and goddess. During fall of 1980 I took Larry's graduate macro-econometrics course and got to know him. He won the Nobel Prize that semester, on a class day, resulting in a classroom filled with television cameras. What a heady mix!

I turned down other offers and stayed at Penn for graduate studies, moving in 1981 from Wharton to Arts and Sciences, home of the Department of Economics and Larry Klein. My  decision to stay at Penn, and to move to the Economics Department, was largely due to Larry's presence there. During the summer following my first year of the Ph.D. program, I worked on a variety of country models for Larry's Project LINK, under his supervision and that of another leading modeler in the Klein tradition, Peter Pauly.  It turned out that the LINK summer job pushed me over the annual salary cap for a graduate student -- $6000 or so 1982 dollars, if I remember correctly -- so Larry and Peter paid me the balance in kind, taking me to the Project LINK annual meeting in Wiesbaden, Germany. More excitement, and also my first trip abroad.

Both Larry and Peter helped supervise my 1986 Penn Ph.D. dissertation, on ARCH modeling of asset return volatility. I couldn't imagine a better trio of advisors: Marc Nerlove as main advisor, with committee members Larry and Peter (who introduced me to ARCH). I then took a job at the Federal Reserve Board, with the Special Studies Section led by Peter Tinsley, a pioneer in optimal control of macro-econometric models. Circa 1986 Larry had more Ph.D. students at the Board than anyone else, by a wide margin. Surely that helped me land the Special Studies job. Another Klein student, my good friend Glenn Rudebusch, also went from Penn to the Board that year, and we wound up co-authoring a dozen articles and two books over some  thirty-five years. 

I returned to Penn in 1989 as an assistant professor. Although I have no behind-the-scenes knowledge, it's hard to imagine that Larry's input didn't contribute to my invitation to return. Those early years were memorable for many things, including econometric socializing. During the 1990's my wife Susan and I had lots of parties at our home for faculty and students. The Kleins were often part of the group, as were Bob and Anita Summers, Herb and Helene Levine, Bobby and Julie Mariano, Jere Behrman and Barbara Ventresco, Jerry Adams, and many more. I recall a big party on one of Penn's annual Economics Days, which that year celebrated The Keynesian Revolution, Larry's landmark 1947 monograph.

The story continues, but I'll mention just one more thing. I was honored and humbled to deliver the Lawrence R. Klein Lecture at the 2005 Project LINK annual meeting in Mexico City, some 25 years after Larry invited a green 22-year-old to observe the 1982 meeting in Wiesbaden.

I have stressed guidance and support, but in closing let me not forget inspiration, which Larry also provided for three decades, in spades. He was the ultimate scholar, focused and steady, and the ultimate gentleman, always gracious, a gentle giant.

Thanks Larry. We look forward to working daily to honor and advance your legacy.

Really useful mini-book.  Thanks Jennifer and David.  Free until Sept 10.  https://bit.ly/2GttdT0

More on: Ed George at 70 Looks Better than I Looked at 45

I may have jumped the gun.  I had assumed that the plan was to stay 2020 but going virtual.  Maybe not.  Maybe 2021 physical.  We'll see.  Keep checking the link.

Ed George at 70 Looks Better than I Looked at 45

Check him out: http://jh-cai.com/edgeorge70/.  So unfair.  Nevertheless I hope you will put my esteemed colleague's 70th birthday conference on your calendar and join the online festivities.  His contributions are diverse and powerful. In many respects his work defines modern applied Bayesian analysis, as well as the modern interface of statistics and machine learning. I am immensely grateful for his leadership in research, in seminars, at dinners, and much more.  His inspirational positivity runs through it all.

Big Data: Updated Historical Note and New BBC Podcast

Updated historical note, "On the Origin(s) and Development of "Big Data": The Phenomenon, the Term, and the Discipline", here.  BBC podcast here.

On High-Dimensional, Non-Linear, Non-Gaussian Continuous-Time Likelihood Evaluation

Tired of trying to beat the particle filter into submission?  Life has gotten a lot easier, at least for evaluating DSGE model likelihoods in continuous time.  See the concluding section of the new and insightful survey, "Estimating DSGE Models: Recent Advances and Future Challenges" by Fernández-Villaverde and Guerrón-Quintana, which echoes section 5.1 of  "Financial Frictions and the Wealth Distribution", by Fernández-Villaverde, Hurtado and Nuño (FVHN).  FVHN show how to take advantage of the mathematical structure of a continuous-time DSGE model to evaluate its associated likelihood with almost no computational effort.  In particular, solution of the Kolmogorov forward equation (the key to likelihood evaluation; see Lo (1987)) simply amounts to transposing and inverting a sparse matrix already computed when solving the model, which makes likelihood evaluation trivial and lightening-fast.

The HAC Emperor Has No Clothes: Part 3

If you've been reading this blog for a while, you know I'm no fan of nonparametric HAC time-series regression.  See here and here.  Instead just do parametric ARMA disturbances or dynamic regressions, select orders using AIC etc., estimate by feasible GLS or MLE.  It's trivial to implement (even in 1985, say) and basically as good as it gets for efficient estimation and inference (and of course it too is nonparametric from a sieve perspective).  Plus you can exploit the parametrically-captured serial correlation for improved time-series prediction.

A new paper by Dick Baillie, George Kapetanios and Kun Ho Kim, "Practical Approaches to Achieve Robust Inference in Time Series Regressions," brings all this into even sharper focus.  Their beautiful Figure 1, reproduced below, says it all.  The black dot is feasible GLS; the reds and blues are the standard nonparametric competitors.  Presumably the paper will be posted soon.  A related much earlier paper is here.