Saturday, April 30, 2016

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


9th Annual Society for Financial Econometrics (SoFiE) Conference 
June 15-17, 2016
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


Preliminary Program

René Garcia, EDHEC Business School

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

Francis X. Diebold, University of Pennsylvania

City University of Hong Kong

Yingying Li, Hong Kong University of Science and Technology
Daniel Preve, City University of Hong Kong
Giorgio Valente, City University of Hong Kong

Jianqing Fan, Princeton University

           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

Sunday, April 24, 2016

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.

Monday, April 18, 2016

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.

Wednesday, April 13, 2016

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.

Sunday, April 10, 2016

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.