Penn Econometrics Colloquium this Saturday

The annual Greater New York Metropolitan Area Econometrics Colloquium will be hosted at Penn, this Saturday 12/7/2019. The program is now available and appears below. 

Saturday, December 7, 2019

The 14th Greater New York Metropolitan Area Econometrics Colloquium

Conference Venue
Forum 250, 2nd Floor,
133 South 36th Street, Philadelphia, PA, 19104
The Ronald O. Perelman Center for Political Science and Economics (PCPSE)
University of Pennsylvania

Organizing Committee

Karun Adusumilli, Xu Cheng, Frank Diebold, Wayne Gao, Frank Schorfheide

Sponsors
Department of Economics, University of Pennsylvania
Penn Institute for Economic Research
Warren Center for Network and Data Sciences

Program
Each presentation is 20 minutes plus 5 minutes discussion

8:30-9:00	Breakfast and Registration
9:00-10:15	Session 1. Chair: Wayne Gao
	“Adaptation Bounds for Confidence Bands under Self-Similarity” by Timothy Armstrong
	“Nonparametric Identification under Independent Component Restrictions” by Ivana Komunjer and Dennis Kristensen
	“Local Projection Inference is Simpler and More Robust Than You Think” by José Luis Montiel Olea and Mikkel Plagborg-Møller
10:15-10:45	Break
10:45-12:00	Session 2. Chair: Karun Adusumilli
	“Identification through Sparsity in Factor Models” by Simon Freyaldenhoven
	“Predictive Properties of Forecast Combination, Ensemble Methods, and Bayesian Predictive Synthesis” by Kosaku Takanashi and Kenichiro McAlinn
	“Learning Latent Factors from Diversified Projections and its Applications to Over-Estimated and Weak Factors” by Jianqing Fan and Yuan Liao
12:00-1:30	Lunch
1:30-2:45	Session 3. Chair: Xu Cheng
	“Bootstrap with Cluster-dependence in Two or More Dimensions” by Konrad Menzel
	“Robust Inference about Conditional Tail Features: A Panel Data Approach” by Yuya Sasaki and Yulong Wang
	“On Binscatter” by Matias Cattaneo, Richard Crump, Max Farrell, and Yingjie Feng
2:45-3:15	Break
3:15-4:30	Session 4. Chair: Frank Schorfheide
	“Estimation in Auction Models with Shape Restrictions” by Joris Pinkse and Karl Schurter
	“Empirical Framework for Cournot Oligopoly with Private Information” by Gaurab Aryal and Federico Zincenko
	“Identification of Structural and Counterfactual Parametersin a Large Class of Structural Econometric Models” by Lixiong Li
4:30-4:45	Break
4:45-6:00	Session 5. Chair: Frank Diebold
	“A Short T Interactive Panel Data Model with Fixed Effects” by Jinyong Hahn and Nese Yildiz
	“Salvaging Falsified Instrumental Variable Models” by Matthew Masten and Alexandre Poirier
	“Bootstrap-Based Inference for Cube Root Asymptotics” by Matias Cattaneo, Michael Jansson, and Kenichi Nagasawa

George Tauchen 70th

Lots of good econometrics at Duke in honor of George Tauchen's 70th! Great conference. Check here for the first pages of some of George's greatest hits, as well as some fantastic "word clouds" summarizing the evolution of his research in the 80's, 90's, 00's, 10's, and all-time. Conference program and papers here. My slides for the Diebold-Rudebusch diurnal temperature range (DTR) paper are below. The link to George's work is studying the daily the daily range as a volatility measure, as for example in Gallant-Hsu-Tauchen, "Using Daily Range Data to Calibrate Volatility Diffusions." In George's case it's an asset return volatility context; in our DTR case it's a climate volatility context.

FRBSF on Climate Change

Just back from the fantastic Federal Reserve Bank of San Francisco Conference on the Economics of Climate Change.  Program and links to papers here.  Every paper was a highlight.  Check out, for example, Basal-Kiku-Ochoa here on the more "structural empirical" side , or Pesaran et al. here on the more "reduced-form empirical" side (although they have theory too). Really good stuff. The slides for my discussion of Pesaran et al. follow.

Hedging Realized vs. Expected Volatility

Not all conferences can be above average, let alone in the extreme right tail of the distribution, so it's wonderful when it happens, as with last week's AP conference. Fine papers all -- timely, thought provoking, and empirically sophisticated.  Thanks to Jan Eberly and Konstantin Milbradt for assembling the program, here (including links to papers). 

I keep thinking about the Dew-Becker-Giglio-Kelly paper. For returns r, they produce evidence that (1) investors are willing to pay a lot to insure against movements in realized volatility, r^2_{t}, but (2) investors are not willing to pay to insure against movements in expected future realized volatility (conditional variance), E_t(r^2_{t+1} | I_t). On the one hand, as a realized volatility guy I'm really intrigued by (1). On the other hand, it seems hard to reconcile (1) and (2), a concern that was raised at the meeting. On the third hand, maybe it's not so hard.  Hmmm...

Big Data in Dynamic Predictive Modeling

Our Journal of Econometrics issue, Big Data in Dynamic Predictive Econometric Modeling, is now in press.  It is partly based on a Penn conference, generously supported by Penn's Warren Center for Network and Data Sciences, University of Chicago's Stevanovich Center for Financial Mathematics, and Penn's Institute for Economic Research.  The intro is here and the paper list is here.

Big Data for 21st Century Economic Statistics

I earlier posted here when the call for papers was announced for the NBER's CRIW meeting on Big Data for 21st Century Economic Statistics. The wheels have been turning, and the meeting will soon transpire. The program is here, with links to papers. [For general info on the CRIW's impressive contributions over the decades, see here.]

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."

Greater New York Area Econometrics Colloquium

Last week's 13th annual Greater New York Area Econometrics Colloquium, generously hosted by Princeton, was a great success, with strong papers throughout. The program is below. I found two papers especially interesting. I already blogged on Spady and Stouli's “Simultaneous Mean-Variance Regression”. The other was "Nonparametric Sample Splitting", by Lee and Wang.

Think of a nonlinear classification problem. In general the decision boundary is of course a highly nonlinear surface, but it's a supervised learning situation, so it's "easy" to learn the surface using standard nonlinear regression methods. Lee and Wang, in contrast, study an unsupervised learning situation, effectively a threshold regression model, where the threshold is determined by an unknown nonparametric relation. And they have very cool applications to things like estimating effective economic borders, gerrymandering, etc. 

The 13th Greater New York Metropolitan Area Econometrics Colloquium

Princeton University, Saturday, December 1, 2018

9.00am-10.30am: Session 1

“Simple Inference for Projections and Linear Programs” by Hiroaki Kaido (BU), Francesca Molinari (Cornell), and Jörg Stoye (Cornell)

“Clustering for multi-dimensional heterogeneity with application to production function estimation” by Xu Cheng (UPenn), Peng Shao (UPenn), and Frank Schorfheide (UPenn)

“Adaptive Bayesian Estimation of Mixed Discrete-Continuous Distributions Under Smoothness and Sparsity” by Andriy Norets (Brown) and Justinas Pelenis (Vienna IAS)

11.00am-12.30pm: Session 2

“Factor-Driven Two-Regime Regression” by Sokbae Lee (Columbia), Yuan Liao (Rutgers), Myung Hwan Seo (Cowles), and Youngki Shin (McMaster)

“Semiparametric Estimation in Continuous-Time: Asymptotics for Integrated Volatility Functionals with Small and Large Bandwidths” by Xiye Yang (Rutgers)

“Nonparametric Sample Splitting” by Yoonseok Lee (Syracuse) and Yulong Wang (Syracuse)

2.00pm-3.30pm: Session 3

“Counterfactual Sensitivity and Robustness” by Timothy Christensen (NYU) and Benjamin Connault (IEX Group)

“Dynamically Optimal Treatment Allocation Using Reinforcement Learning” by Karun Adusumilli (UPenn), Friedrich Geiecke (LSE), and Claudio Schilter (LSE)

“Simultaneous Mean-Variance Regression” by Richard Spady (Johns Hopkins) and Sami Stouli (Bristol)

4.00pm-5.30pm: Session 4

“Semi-parametric instrument-free demand estimation: relaxing optimality and equilibrium assumptions” by Sungjin Cho (Seoul National), Gong Lee (Georgetown), John Rust (Georgetown), and Mengkai Yu (Georgetown)

“Nonparametric analysis of monotone choice” by Natalia Lazzati (UCSC), John Quah (Johns Hopkins), and Koji Shirai (Kwansei Gakuin)

“Discrete Choice under Risk with Limited Consideration” by Levon Barseghyan (Cornell), Francesca Molinari (Cornell), and Matthew Thirkettle (Cornell)

Organizing Committee

Bo Honoré, Michal Kolesár, Ulrich Müller, and Mikkel Plagborg-Møller

Participants Adusumilli  Karun UPenn
Althoff	Lukas	Princeton
Anderson	Rachel	Princeton
Bai	Jushan	Columbia
Beresteanu	Arie	Pitt
Callaway	Brantly	Temple
Chao	John	Maryland
Cheng	Xu	UPenn
Choi	Jungjun	Rutgers
Choi	Sung Hoon	Rutgers
Cox	Gregory	Columbia
Christensen	Timothy	NYU
Diebold	Frank	UPenn
Dou	Liyu	Princeton
Gao	Wayne	Yale
Gaurav	Abhishek	Princeton
Henry	Marc	Penn State
Ho	Paul	Princeton
Honoré	Bo	Princeton
Hu	Yingyao	Johns Hopkins
Kolesar	Michal	Princeton
Lazzati	Natalia	UCSC
Lee	Simon	Columbia
Li	Dake	Princeton
Li	Lixiong	Penn State
Liao	Yuan	Rutgers
Menzel	Konrad	NYU
Molinari	Francesca	Cornell
Montiel Olea	José Luis	Columbia
Müller	Ulrich	Princeton
Norets	Andriy	Brown
Plagborg-Møller	Mikkel	Princeton
Poirier	Alexandre	Georgetown
Quah	John	Johns Hopkins
Rust	John	Georgetown
Schorfheide	Frank	UPenn
Seo	Myung	SNU & Cowles
Shin	Youngki	McMaster
Sims	Christopher	Princeton
Spady	Richard	Johns Hopkins
Stoye	Jörg	Cornell
Taylor	Larry	Lehigh
Vinod	Hrishikesh	Fordham
Wang	Yulong	Syracuse
Yang	Xiye	Rutgers
Zeleneev	Andrei	Princeton

JFEC Special Issue for Peter Christoffersen

No, I have not gone into seclusion. Well actually I have, but not intentionally and certainly not for lack of interest in the blog. Just the usual crazy time of year, only worse this year for some reason. Anyway I'll be back very soon, with lots to say! But here's something important and timely, so it can't wait:

Journal of Financial Econometrics

Call for Papers

Special Issue in Honor of Peter Christoffersen

The Journal of Financial Econometrics is organizing a special issue in memory of Professor Peter
Christoffersen, our friend and colleague, who passed away in June 2018. Peter held the TMX Chair in Capital Markets and a Bank of Canada Fellowship and was a widely respected member of the Rotman School at the University of Toronto since 2010. Prior to 2010, Peter was a valued member of the Desautels Faculty of Management at McGill University. In addition to his transformative work in econometrics and volatility models, financial risk and financial innovation had been the focus of Peter’s work in recent years.

We invite paper submissions on topics related to Peter’s contributions to Finance and Econometrics. We are particularly interested in papers related to the following topics:

1)   The use of option-implied information for forecasting; Rare disasters and portfolio
management; Factor structures in derivatives and futures markets.

2)   Volatility, correlation, extreme events, systemic risk and Value-at-Risk modeling for
financial market risk management.

3)   The econometrics of digital assets; Big data and Machine Learning.

To submit a paper, authors should login to the Journal of Financial Econometrics online submission system and follow the submission instructions as per journal policy.  The due date for submissions is June 30, 2019.  It is important to specify in the cover letter that the paper is submitted to the special issue in honor of Peter Christoffersen, otherwise your paper will not be assigned to the guest editors.

Guest Editors

•    Francis X. Diebold, University of Pennsylvania

•    René Garcia, Université de Montréal and Toulouse School of Economics

•    Kris Jacobs, University of Houston

Machine Learning, Volatility, and the Interface

Just got back from the NBER Summer Institute. Lots of good stuff happening in the Forecasting and Empirical Methods group. The program, with links to papers, is here.

Lots of room for extensions too. Here's a great example. Consider the interface of the Gu-Kelly-Xiu and Bollerslev-Patton-Quagvleg papers. At first you might think that there is no interface. 

Kelly-Xiu is about using off-the-shelf machine-learning methods to model risk premia in financial markets; that is, to construct portfolios that deliver superior performance. (I had guessed they'd get nothing, but I was massively wrong.) Bollerslev et al. is about predicting realized covariance by exploiting info on past signs (e.g., was yesterday's covariance cross-product pos-pos, neg-neg, pos-neg, or neg-pos?). (They also get tremendous results.)

But there's actually a big interface.

Note that Kelly-Xiu is about conditional mean dynamics -- uncovering the determinants of expected excess returns. You might expect even better results for derivative assets, as the volatility dynamics that drive options prices may be nonlinear in ways missed by standard volatility models. And that's exactly the flavor of the Bollerslev et al. results -- they find that a tree structure conditioning on sign is massively successful.

But Bollerslev et al. don't do any machine learning. Instead they basically stumble upon their result, guided by their fine intuition. So here's a fascinating issue to explore: Hit the Bollerslev et al. realized covariance data with machine learning (in particular, tree methods like random forests) and see what happens. Does it "discover" the Bollerslev et al. result? If not, why not, and what does it discover? Does it improve upon Bollerslev et al.?

Climate Change and NYU Volatility Institute

There is little doubt that climate change -- tracking, assessment, and hopefully its eventual mitigation -- is the burning issue of our times. Perhaps surprisingly, time-series econometric methods have much to offer for weather and climatological modeling (e.g., here), and several econometric groups in the UK, Denmark, and elsewhere have been pushing the agenda forward.

Now the NYU Volatility Institute is firmly on board. A couple months ago I was at their most recent annual conference, "A Financial Approach to Climate Risk", but it somehow fell through the proverbial (blogging) cracks. The program is here, with links to many papers, slides, and videos. Two highlights, among many, were the presentations by Jim Stock (insights on the climate debate gleaned from econometric tools, slides here) and Bob Litterman (an asset-pricing perspective on the social cost of climate change, paper here). A fine initiative!

Machines Learning Finance

FRB Atlanta recently hosted a meeting on "Machines Learning Finance". Kind of an ominous, threatening (Orwellian?) title, but there were lots of (non-threatening...) pieces. I found the surveys by Ryan Adams and John Cunningham particularly entertaining. A clear theme on display throughout the meeting was that "supervised learning" -- the main strand of machine learning -- is just function estimation, and in particular, conditional mean estimation. That is, regression. It may involve high dimensions, non-linearities, binary variables, etc., but at the end of the day it's still just regression. If you're a regular No Hesitations reader, the "insight" that supervised learning = regression will hardly be novel to you, but still it's good to see it disseminating widely.

Monetary Policy and Global Spillovers

The Bank of Chile's latest Annual Conference volume, Monetary Policy and Global Spillovers: Mechanisms, Effects, and Policy Measures, is now out, here.  In addition to the research presented in the volume, I love the picture on its front cover. So peaceful.

Fourth Penn Quantitative Policy Workshop

Some years ago I blogged on the first Workshop on Quantitative Tools for Macroeconomic Policy Analysis hosted by the Penn Institute for Economic Research (PIER). We just completed the fourth! It was a great group as usual, with approximately 25 participants from around the globe, mostly economists at country central banks, ECB, etc. Some of the happy campers, along with yours truly, appear in the photo. You can find all sorts of information on the workshop site. Information / registration for the next Workshop (May 2019) will presumably be posted in fall. Please consider joining us, and tell your friends!

2017 NBER-NSF Time Series Meeting

Just back from 2017 NBER-NSF Time Series at Northwestern.  Quite a feast -- my head is spinning.  Program dumped below; formatted version here.  Many thanks to the program committee for producing this event, and more generally for keeping the series going, year after year, stronger than ever.  (See here for some history and links to past locations, programs, etc.)

The papers were very strong.  Among those that I found particularly interesting are:

-- Moon.  Forecasting in short panels.  You'd think it would be impossible since you need the individual effects.  But it's not.

“Forecasting with Dynamic Panel Data Models”, Hyungsik Roger Moon (University of Southern California), Laura Liu, and Frank Schorfheide

-- Shephard.  Causal estimation meets time series.

“Time series experiments, causal estimands and exact p-values”, Neil Shephard (Harvard University) and Iavor Bojinov

-- The entire (and marvelously-coherent) "Lumsdaine Sesssion" (Pruitt, Pelger, Giglio).  Real progress on econometric methods for identifying financial-market risk factors, with sharp empirical results. 

“Instrumented Principal Component Analysis”, Seth Pruitt (Arizona State University), Bryan Kelly, and Yinan Su
“Estimating Latent Asset-Pricing Factors”, Markus Pelger (Stanford University) and Martin Lettau

“Inference on Risk Premia in the Presence of Omitted Factors”, Stefano Giglio (University of Chicago) and Dacheng Xiu


------------------

2017 NBER-NSF Time Series Conference
Friday, September 8 – Saturday, September 9
Kellogg School of Management
Kellogg Global Hub
2211 N Campus Drive; Evanston, IL 60208
Friday, September 8
Registration begins 10:20am (White Auditorium)
Welcome and opening remarks: 10:50am
Session 1: 11:00am – 12:30pm
Chair: Ruey S. Tsay (University of Chicago)
 “Egalitarian Lasso for Shrinkage and Selection in Forecast Combination” Francis X. Diebold (University of Pennsylvania) and Minchul Shin
 “Forecasting with Dynamic Panel Data Models” Hyungsik Roger Moon (University of Southern California), Laura Liu, and Frank Schorfheide
 “Large Vector Autoregressions with Stochastic Volatility and Flexible Priors” Andrea Carriero (Queen Mary University of London), Todd E. Clark, and Massimiliano Marcellino
12:30pm - 2:00pm: Lunch and Poster Session 1 (Faculty Summit, 4th Floor)
 “The Dynamics of Expected Returns: Evidence from Multi-Scale Time Series Modeling“ Daniele Bianchi (University of Warwick)
 “Testing for Unit-root Non-stationarity against Threshold Stationarity” Kung-Sik Chan (University of Iowa)
 “Group Orthogonal Greedy Algorithm for Change-point Estimation of Multivariate Time Series” Ngai Hang Chan (The Chinese University of Hong Kong)
 “The Impact of Waiting Times on Volatility Filtering and Dynamic Portfolio Allocation” Dobrislav Dobrev (Federal Reserve Board of Governors)
 “Testing for Mutually Exciting Jumps and Financial Flights in High Frequency Data” Mardi Dungey (University of Tasmania), Xiye Yang (Rutgers University) presenting
 “Pockets of Predictability” Leland E. Farmer (University of California, San Diego)
 “Factor Models of Arbitrary Strength” Simon Freyaldenhoven (Brown University)
 “Inference for VARs Identified with Sign Restrictions” Eleonora Granziera (Bank of Finland)
 “The Time-Varying Effects of Conventional and Unconventional Monetary Policy: Results from a New Identification Procedure” Atsushi Inoue (Vanderbilt University)
 “On spectral density estimation via nonlinear wavelet methods for non-Gaussian linear processes” Linyuan Li (University of New Hampshire)
 “Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting” Kenichiro McAlinn (Duke University)
 “Periodic dynamic factor models: Estimation approaches and applications” Vladas Pipiras (University of North Carolina)
 “Canonical stochastic cycles and band-pass filters for multivariate time series” Thomas M. Trimbur (U. S. Census Bureau)
Session 2: 2:00pm - 3:30pm
Chair: Giorgio Primiceri (Northwestern University)
 “Understanding the Sources of Macroeconomic Uncertainty” Tatevik Sekhposyan (Texas A&M University), Barbara Rossi, and Matthieu Soupre
 “Safety, Liquidity, and the Natural Rate of Interest” Marco Del Negro (Federal Reserve Bank of New York), Domenico Giannone, Marc P. Giannoni, and Andrea Tambalotti
 “Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks” Christiane Baumeister (University of Notre Dame) and James D. Hamilton
Afternoon Break: 3:30pm-4:00pm
Session 3: 4:00pm – 5:30pm
Chair: Serena Ng (Columbia University)
 “Controlling the Size of Autocorrelation Robust Tests” Benedikt M. Pötscher (University of Vienna) and David Preinerstorfer
 “Heteroskedasticity Autocorrelation Robust Inference in Time Series” Regressions with Missing Data Timothy J. Vogelsang (Michigan State University) and Seung-Hwa Rho
 “Time series experiments, causal estimands and exact p-values” Neil Shephard (Harvard University) and Iavor Bojinov
5:30pm – 7pm: Cocktail Reception and Poster Session 2 (Faculty Summit, 4th Floor)
 “Macro Risks and the Term Structure of Interest Rates” Andrey Ermolov (Fordham University)
 “Holdings-based Fund Performance Measures: Estimation and Inference” Wayne E. Ferson (University of Southern California), Junbo L. Wang (Louisiana State University) presenting
 “Economic Predictions with Big Data: The Illusion of Sparsity” Domenico Giannone (Federal Reserve Bank of New York)
 “Estimation and Inference of Dynamic Structural Factor Models with Over-identifying Restrictions” Xu Han (City University of Hong Kong)
 “Bayesian Predictive Synthesis: Forecast Calibration and Combination” Matthew C. Johnson (Duke University)
 “Time Series Modeling on Dynamic Networks” Jonas Krampe (TU Braunschweig)
 “The Complexity of Bank Holding Companies: A Topological Approach” Robin L. Lumsdaine (American University)
 “Sieve Estimation of Option Implied State Price Density” Zhongjun Qu (Boston University) - Junwen Lu (Boston University) presenting
 “Linear Factor Models and the Estimation of Expected Returns” Cisil Sarisoy (Northwestern University)
 “Efficient Parameter Estimation for Multivariate Jump-Diffusions” Gustavo Schwenkler (Boston University)
 “News-Driven Uncertainty Fluctuations” Dongho Song (Boston College)
 “Contagion, Systemic Risk and Diagnostic Tests in Large Mixed Panels” Cindy S.H. Wang (National Tsing Hua University and CORE, University Catholique de Louvain)
7-10pm: Dinner (White Auditorium)
 Dinner speaker: Nobel Laureate Robert F. Engle
Saturday, September 9
Continental Breakfast: 8:00am – 8:30am
Registration begins 8:30am (White Auditorium)
Session 4: 9:00am – 10:30am
Chair: Thomas Severini (Northwestern University)
 “Estimation of time varying covariance matrices for large datasets” Liudas Giraitis (Queen Mary University of London), Y. Dendramis, and G. Kapetanios
 “Indirect Inference With(Out) Constraints” Eric Renault (Brown University) and David T. Frazier
 “Edgeworth expansions for a class of spectral density estimators and their applications to interval estimation” S.N. Lahiri (North Carolina State University) and A. Chatterjee
Morning Break: 10:30am-11:00am
Session 5: 11:00am-12:30pm
Chair: Robin L. Lumsdaine (American University)
 “Instrumented Principal Component Analysis” Seth Pruitt (Arizona State University), Bryan Kelly, and Yinan Su
 “Estimating Latent Asset-Pricing Factors” Markus Pelger (Stanford University) and Martin Lettau
 “Inference on Risk Premia in the Presence of Omitted Factors” Stefano Giglio (University of Chicago) and Dacheng Xiu
12:30pm-2pm: Lunch and Poster Session 3 (Faculty Summit, 4th Floor)
 “Regularizing Bayesian Predictive Regressions” Guanhao Feng (City University of Hong Kong)
 “Good Jumps, Bad Jumps, and Conditional Equity Premium” Hui Guo (University of Cincinnati)
 “High-dimensional Linear Regression for Dependent Observations with Application to Nowcasting” Yuefeng Han (The University of Chicago)
 “Maximum Likelihood Estimation for Integer-valued Asymmetric GARCH (INAGARCH) Models” Xiaofei Hu (BMO Harris Bank, N.A.)
 “Tail Risk in Momentum Strategy Returns” Soohun Kim (Georgia Institute of Technology)
 “The Perils of Counterfactual Analysis with Integrated Processes” Marcelo C. Medeiros (Pontifical Catholic University of Rio de Janeiro) and Ricardo Masini (Pontifical Catholic University of Rio de Janeiro)
 “Anxious unit root processes” Jon Michel (The Ohio State University)
 “Limiting Local Powers and Power Envelopes of Panel AR and MA Unit Root Tests” Katsuto Tanaka (Gakushuin University)
 “High-Frequency Cross-Market Trading: Model Free Measurement and Applications”
Ernst Schaumburg (AQR Capital Management, LLC) – Dobrislav Dobrev (Federal Reserve Board of Governors) presenting
 “A persistence-based Wold-type decomposition for stationary time series” Claudio Tebaldi (Bocconi University)
 “Necessary and Sufficient Conditions for Solving Multivariate Linear Rational Expectations Models and Factoring Matrix Polynomials” Peter A. Zadrozny (Bureau of Labor Statistics)
Session 6: 2:00pm – 3:30pm
Chair: Beth Andrews (Northwestern University)
 “Models for Time Series of Counts with Shape Constraints” Richard A. Davis (Columbia University) and Jing Zhang
 “Computationally Efficient Distribution Theory for Bayesian Inference of High-Dimensional Dependent Count-Valued Data” Scott H. Holan (University of Missouri, U.S. Census Bureau), Jonathan R. Bradley, and Christopher K. Wikle
 “Functional Autoregression for Sparsely Sampled Data”
Daniel R. Kowal (Cornell University, Rice University)