SoFiE 2016 Hong Kong

Still not too late to register!  I hope to see you there.

REGISTRATION_9th Annual SoFiE Conference_June 14-17, 2016, Hong Kong

REGISTRATION 9th Annual Society for Financial Econometrics (SoFiE) Conference  June 15-17, 2016   and   Pre-Conference for Young Scholars June 14, 2016 Hosted by the Department of Economics and Finance & the Global Research Unit (GRU) at City University of Hong Kong _________________________________________________________________________ CONFERENCE WEBSITE & REGISTRATION https://www.cb.cityu.edu.hk/conference/2016-sofie/ Preliminary Program ___________________________________________________ HALBERT WHITE JR. MEMORIAL JFEC INVITED LECTURE René Garcia, EDHEC Business School INVITED SPEAKERS Harrison Hong, Princeton University Michael McCracken, Federal Reserve Bank of St. Louis Per Mykland, University of Chicago Olivier Scaillet, University of Geneva & Swiss Finance Institute Hao Zhou, PBCSF, Tsinghua University INVITED SPEAKER, Pre-Conference Francis X. Diebold, University of Pennsylvania CONFERENCE LOCATION City University of Hong Kong LOCAL ORGANIZERS Yingying Li, Hong Kong University of Science and Technology Daniel Preve, City University of Hong Kong Giorgio Valente, City University of Hong Kong PROGRAM CHAIR Jianqing Fan, Princeton University PROGRAM COMMITTEE            Torben Andersen, Northwestern University           Frank Kleibergen, University of Amsterdam            Heather Anderson, Monash University           Ralph Koijen, London Business School            Elena Andreou, University of Cyprus           Siem Jan Koopman, VU University Amsterdam            Jushan Bai, Columbia University           Stephen Kou, National University of Singapore            Gurdip Bakshi, University of Maryland           David Lando, Copenhagen Business School            Federico Bandi, Johns Hopkins University           Jia Li, Duke University            David Bates, University of Iowa           Yingying Li, Hong Kong University of Science & Technology            Luc Bauwens, Universite Catholique de Louvain           Haitao Li, Cheung Kong Graduate School of Business            Luca Benzoni, Federal Reserve Bank of Chicago           Yuan Liao, University of Maryland            Tim Bollerslev, Duke University           Chenghu Ma, Fudan University            Laurent Calvet, HEC Paris           Loriano Mancini, EPFL            Marine Carrasco, University of Montreal           Cecilia Mancini, University of Florence            Rong Chen, Rutgers University           Simone Manganelli, European Central Bank            Mikhail Chernov, London School of Economics & UCLA           Nour Meddahi,  Toulouse School of Economics            Peter Christoffersen, University of Toronto           Per Mykland, University of Chicago            Riccardo Colacito, University of North Carolina, Chapel Hill           Joon Park, Indiana University             Pierre Collin-Dufresne, EPFL           Andrew Patton, NYU Stern            Valentina Corradi, University of Surrey            Tarun Ramadorai, University of Oxford            Walter Distaso, Imperial College Business School           Eric Renault, Brown University            Dobrislav Dobrev, Federal Reserve Board           Michael Rockinger, University of Lausanne            Yanqin Fan, University of Washington, Seattle           Jeroen Rombouts, ESSEC Business School            Patrick Gagliardini, University of Lugano           Mathieu Rosenbaum, University Pierre et Marie Curie (Paris 6)            Rene Garcia, EDHEC Business School           Barbara Rossi, Pompeu Fabra University            Eric Ghysels, University of North Carolina, Chapel Hill           Jeffrey Russell, University of Chicago            Sílvia Goncalves, University of Montreal           Olivier Scaillet, University of Geneva & SFI            Christian Gourieroux, University of Toronto           Paul Schneider, University of Lugano & SFI            Anthony Hall, University of Technology Sydney           Enrique Sentana, CEMFI            Wolfgang Hardle, Humboldt University           George Tauchen, Duke University            Joel Hasbrouck, NYU Stern           Allan Timmermann, University of California, San Diego            Nikolaus Hautsch, University of Vienna           Viktor Todorov, Northwestern University            Stan Hurn, Queensland University of Technology            Fabio Trojani, University of Lugano & SFI            Ravi Jagannathan, Northwestern University            Ruey Tsay, Duke University of Chicago            Christopher S. Jones, University of Southern California             Bas J.M. Werker, Tilburg University            Scott Joslin, University of Southern California            Dacheng Xiu, University of Chicago            Raymond Kan, University of Toronto            Jun Yu, Singapore Management University            Kamhon Kan, Academia Sinica            Zhengjun Zhang, University of Wisconsin            Bryan Kelly, University of Chicago            Lan Zhang, University of Illinois at Chicago            Lutz Kilian, University of Michigan            Harold H. Zhang, University of Texas at Dallas            Robert Kimmel, National University of Singapore            Xunyu Zhou, Oxford University

The Distribution of Global Economic Activity...

... as proxied by the global distribution of nighttime lights (from a fascinating new paper by Hendersen et al.).  Like many good graphics, this one repays careful study.  You'll see lots of places where the lights match your prior, but you'll also see places that are perhaps "surprisingly" well-lit relative to popular perceptions (e.g., central America), other places that are perhaps surprisingly dark (e.g., most of Russia), fascinating patterns (e.g., look at Europe stretching east into Russia), etc.

On the Real-Time GDP War

A few days ago the WSJ did an interesting piece, Fed Banks Spar Over GDP Data, highlighting that the "race to provide credible real-time data on U.S. economic growth is pitting the Federal Reserve Bank of New York against its sibling in Atlanta."

In all this, real-time data on "economic growth" is interpreted as real-time data on GDP growth.

In my opinion, all of the real-time GDP products basically reflect a misguided perspective if the goal is real-time tracking of economic growth (which is as it should be, and what is claimed). If you want to track real-time growth, you should be tracking an extraction of a broad dynamic factor, effectively averaging over many indicators, not just tracking real-time GDP. That has been the leading and invaluable perspective from Burns and Mitchell straight through to modern dynamic-factor approaches.  My favorite, of course, is the FRB Philadelphia's ADS Index, but there are many others.

Big Data: Tall, Wide, and Dense

It strikes me that "tall", "wide", and "dense" might be useful words and conceptualizations of aspects of Big Data relevant in time-series econometrics.

Think of a  regression situation, with a  (T x K) "X matrix" for  T "days" (or whatever) of data for each of K variables.  Now imagine sampling intra-day, m times per day.  Then  X is (mT x K).  Big data correspond to huge-X situations arising because one or more of T, K, and m is huge.  (Of course there will always be subjectivity associated with "how huge is huge".)

T, K, and m are usefully considered separately.

-- As T gets large we have "tall data" (in reference to the tall X matrix, due to the large number of time periods, i.e., the long calendar span of data)

-- As K gets large we have "wide data" (in reference to the wide X matrix due to the large number of regressors)

-- As m gets large we have "dense data" (in reference to the high-frequency intra-day sampling, regardless of whether the data are tall)

A few examples:

--  Consider 2500 days of 1-minute returns for each of 5000 stocks.  The data are tall, wide and dense.

--  Consider 25 days of 1-minute returns for each of 50 stocks.  The data are dense, but neither tall nor wide.

--  Consider 2500 days of daily returns for each of 5000 stocks.   The data are tall and wide, but not dense.

On "The Human Capital Approach to Inference"

Check out the interesting new paper by Bentley MacLeod at Columbia ("The Human Capital Approach to Inference"), on using economic theory in combination with machine learning to estimate conditional average treatment effects better than can be done with randomized control trials.

Quite apart from new methods for accurate estimation of conditional average treatment effects, the paper's intro contains some interesting tidbits on causal econometric inference. Here's one sequence in yellow, with my reactions:

BM: "There are two distinct approaches to modern empirical economics."
-- The MacLeod paper is exclusively about causal inference, so it should say "two distinct approaches to causal inference in modern empirical economics." Equating causal inference to all of empirical economics is simply wrong. Causal inference is a large and very important part of modern empirical economics, but far from its entirety. The booming field of financial econometrics, for example, is largely and intentionally reduced-form. See this.

BM: "First, there is research using structural models that begins by assuming individuals make utility maximizing decisions within a well defined environment, and then proceeds to measure the value of the unknown parameters..."
-- There is some unsettling truth here. A cynical but not-entirely-false view is that structural causal inference effectively assumes a causal mechanism, known up to a vector of parameters that can be estimated. Big assumption. And of course different structural modelers can make different assumptions and get different results.

BM: "The second approach addresses the self-selection of individuals into different observed treatments or choices by either explicitly randomizing treatments/choices in the context of an experiment...or through the use of a natural experiment that allows for an instrumental variables strategy. There is general agreement that explicit randomization provides one of the cleanest ways to obtain a measure of the effect of choice."
-- There's rarely general agreement about anything in economics. But yes, randomization is arguably the gold standard for causal effect estimation, if and when it can be done credibly.