Wednesday, June 3, 2020

Fine New Macro-Econometrics Toolbox

Check out the cutting-edge Ferroni and Canova toolbox and related materials, here.

From the abstract of their User's Guide:
We use MATLAB functions and routines to estimate VARs, FAVARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density forecasts, and to trace out the causal effect of shocks using a number of identification schemes. The toolbox is equipped to handle missing observations. It can also deal with panels of time series. We describe the methodology employed and implementation of the functions. We illustrate the main features with a number of practical examples.
Looking forward to R, Python, and Julia versions!

Monday, June 1, 2020

Yield Curve Construction

An interesting new paper by Liu and Wu, https://www.nber.org/papers/w27266? and below, is the latest in traditional two-step yield curve construction (summarize bond prices with synthetic zero-coupon yields, and then treat the zero-coupon yields as data).  I wonder how their results would compare to the one-step approach of Andreasen, Christensen, and Rudebusch (2019), which directly analyzes the universe of bond prices,  https://www.sciencedirect.com/science/article/abs/pii/S0304407619300740 and below.  The one-step approach seems highly appealing. Liu and Wu may have built a better mousetrap, but Andreasen, Christensen, and Rudebusch arguably dispense with the need for a mousetrap.


Reconstructing the Yield Curve

Yan LiuJing Cynthia Wu

NBER Working Paper No. 27266
Issued in May 2020
NBER Program(s):Asset PricingEconomic Fluctuations and GrowthMonetary Economics
The constant-maturity zero-coupon Treasury yield curve is one of the most studied datasets. We construct a new dataset using a non-parametric kernel-smoothing method with a novel adaptive bandwidth specifically designed to fit the Treasury yield curve. Our curve is globally smooth while still capturing important local variation. Economically, we show that applying our data leads to different conclusions from using the leading alternative data of Gurkaynak et al. (2007) (GSW) when we repeat two popular studies of Cochrane and Piazzesi (2005) and Giglio and Kelly (2018). Statistically, we show our dataset preserves information in the raw data and has much smaller pricing errors than GSW. Our new yield curve is maintained and updated online, complemented by bandwidths that summarize information content in the raw data: https://sites.google.com/view/jingcynthiawu/yield-data.

Term Structure Analysis with Big Data: One-Step Estimation Using Bond Prices







Abstract

Nearly all studies that analyze the term structure of interest rates take a two-step approach. First, actual bond prices are summarized by interpolated synthetic zero-coupon yields, and second, some of these yields are used as the source data for further empirical examination. In contrast, we consider the advantages of a one-step approach that directly analyzes the universe of bond prices. To illustrate the feasibility and desirability of the one-step approach, we compare arbitrage-free dynamic term structure models estimated using both approaches. We also provide a simulation study showing that a one-step approach can extract the information in large panels of bond prices and avoid any arbitrary noise introduced from a first-stage interpolation of yields.

Wednesday, May 27, 2020

COVID-19 Research From Penn Economics

Here's a  gallery of cutting-edge COVID-19 research from Penn Econ faculty.  I just bumped into it randomly on the Department site.  The round "medals" are cute.  Congrats to the medalists for helping us navigate the new and rough terrain.   https://economics.sas.upenn.edu/pier/covid-19-research

Tuesday, May 26, 2020

New SoFiE Online Seminar Series

From Tim Bollerslev (SoFiE president) and Andrew Patton (seminar organizer):

The series will feature bi-monthly virtual presentations of cutting-edge research in financial econometrics. Presentations will be followed by discussion and audience participation. Seminars will be held on Mondays, from 11am-noon EDT. Recognizing that this time slot may not work for everyone around the globe, the seminars will be recorded and available on SoFiE’s YouTube channel a few days after each event.

SoFiE Seminars will be held as Zoom webinars and are open to anyone with an appetite for research in financial econometrics: professors, students, academics, and non-academics. To receive updates about these events, email sofie@stern.nyu.edu and join our mailing list.

Our first seminar will be on June 1st, with a presentation by Professor Viktor Todorov of Northwestern University, and discussion from Professor Walter Distaso of Imperial College London. The seminar series website contains details on future seminars.

Peter Christoffersen JFEC Issue in Press

Actually it's two issues of JFEC (Journal of Financial Econometrics). The first, Part I, is in press.  Editors' intro below.


Predictive Modeling, Volatility, and Risk Management in Financial Markets
In Memory of Peter F. Christoffersen

Peter F. Christoffersen left us in 2018, much too soon, at the age of 51. He was a world-renowned financial econometrics researcher, educator, lecturer, administrator (including hosting the 2014 SoFiE conference at the University of Toronto), and public servant (including the U.S. Federal Reserve System's Model Validation Committee, charged with reviewing the models used for bank supervision and regulation). If Peter was an esteemed colleague, he was equally a dear friend. His unbridled optimism, relaxed personality, and remarkable humility endeared him to all who knew him.

We honor Peter's path-breaking research in this special issue. Its style is marked by a masterful blend of intuition, theoretical rigor, and always, empirical relevance. It influenced and inspired countless others in academics and industry, world-wide. It has four basic, and highly-intertwined, organizational themes:

1. Predictive models and their evaluation (e.g., his classic early work on evaluating the conditional calibration of interval forecasts, Christoffersen (1998), one of the International Economic Review's ten most-cited papers since its founding in 1960)

2. Financial market risk measurement and management (e.g., his celebrated text, Christofffersen (2003))

3. Asset return volatility modeling and forecasting (e.g., his survey, Andersen et al. (2013))

4. Financial derivative markets with emphasis on options (e.g., Christoffersen et al. (2009), one of his many widely-cited papers).

We humbly offer this two-part special issue as a tribute to Peter. The included papers reflect his style and interests, not only methodologically as characterized above, but also in their wide variety of substantive applications, clearly testifying to the depth and breadth of the Christoffersen legacy.

Francis X. Diebold
University of Pennsylvania

Rene Garcia
University of Montreal

Kris Jacobs
University of Houston

References
Andersen, T.G., T. Bollerslev, P.F. Christoffersen, and F.X. Diebold (2013), "Financial Risk
Measurement for Financial Risk Management," In G. Constantinedes, M. Harris, and R.
Stulz (eds.), Handbook of the Economics of Finance, Elsevier, 1127-1220.

Christoffersen, P. (1998), "Evaluating Interval Forecasts," International Economic Review,
39, 841-862.

Christoffersen, P.F. (2003), Elements of Financial Risk Management, Academic Press.

Christoffersen, P.F., S. Heston, and K. Jacobs (2009), "The Shape and Term Structure of the Index Option Smirk: Why Multifactor Stochastic Volatility Models Work so Well," Management Science, 55, 1914-1932.

Friday, May 22, 2020

No Hesitations Returns

It's been a long enough break -- No Hesitations is back.  I'm going to keep posts very short, Twitter style.  By the way, they get automatically posted to Twitter, so an easy way to follow the blog is simply to follow me on Twitter.  Meanwhile some humor for these crazy times:  Check out this letter, which we've all received ten times in the last month.
https://www.mcsweeneys.net/articles/a-note-from-your-university-about-its-plans-for-next-semester

Sunday, January 26, 2020

No Hesitations Hiatus

It's better to burn out than to fade away.
Neil said that. Or something like that.


It's been a great run and a tremendously rewarding experience, and I strongly suspect that I'll return one day. But I just don't have much to say at the moment, so I think it's time for a break. 

THANKS for your kind and invigorating and unfailing support.  Thinking with you for five years or so has been a great, great pleasure.

I'll be back.
Arnold said that. Or something like that.


Tuesday, January 7, 2020

Ice-Free Arctic Summers are Coming VERY Soon

A very happy New Year to all!

Here's a new D&R to start it off:

"Probability Assessments of an Ice-Free Arctic: 
Comparing Statistical and Climate Model Projections"
by
Francis X. Diebold and Glenn D. Rudebusch
arXiv:1912.10774 [stat.APecon.EM].

The downward trend in Arctic sea ice is a key factor determining the pace and intensity of future global climate change; moreover, declines in sea ice can have a wide range of additional environmental and economic consequences. Based on several decades of satellite data, in a new paper Glenn Rudebusch and I provide statistical forecasts of Arctic sea ice extent during the rest of this century (Diebold and Rudebusch, "Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections", arXiv:1912.10774 [stat.APecon.EM]). Our results indicate that sea ice is diminishing at an increasing rate, in sharp contrast to average projections from the CMIP5 global climate models, which foresee a gradual slowing of sea ice loss even in high carbon emissions scenarios. Our long-range statistical projections also deliver probability assessments of the timing of an ice-free Arctic. This analysis indicates almost a 60 percent chance of a seasonally ice-free Arctic Ocean in the 2030s -- much earlier than the average projection from global climate models.

Friday, December 27, 2019

Holiday Haze


File:Happy Holidays (5318408861).jpg




Your blogger will be back in the New Year. 

Meanwhile, Happy Holidays to all!

Wednesday, December 4, 2019

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. 


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
8:30-9:00Breakfast and Registration
9:00-10:15Session 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
10:15-10:45Break
10:45-12:00Session 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
12:00-1:30Lunch
1:30-2:45Session 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
2:45-3:15Break
3:15-4:30Session 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
4:30-4:45Break
4:45-6:00Session 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