Lisa D Cook: Welcoming remarks - 5th Annual Conference on
"Nontraditional Data, Machine Learning, and Natural Language
Processing in Macroeconomics"
Welcoming remarks by Ms Lisa D Cook, Member of the Board of Governors of the
Federal Reserve System, at the 5th Annual Conference on "Nontraditional Data,
Machine Learning, and Natural Language Processing in Macroeconomics", Washington
DC, 13 November 2023.
* * *
Good morning, and welcome to the 5th annual conference on "Nontraditional Data,
1
Machine Learning, and Natural Language Processing in Macroeconomics." In a world
with evolving data sources and significant advances in computing, it is important to
constantly improve the conceptual framework that macroeconomic policymakers use to
inform their critical decisions. This conference brings academics and practitioners
together to think carefully about that framework and to push for advancements that
could lead to improved economic outcomes.
The Federal Reserve hosted the inaugural edition of this gathering in 2019. Since then,
the conference has evolved into an international partnership between academics,
central banks, statistical agencies, and international organizations, with the Bank of
Italy, the Bank of Canada, and the Swedish Riksbank hosting recent editions. The
conference is also now jointly organized with the Central Bank Research Association
and the Economics with Nontraditional Data and Analytical Tools program, which will
further enhance its international scope.
We are grateful for the international and domestic partnerships that have emerged from
this event and its predecessors. Today, we have about 90 in-person participants in
attendance, representing 30 agencies and foreign central banks. As we continue to
leverage new data and master emerging techniques, cooperation between those that
provide the statistics, those that base policy upon them, and those that leverage the
latest methodologies to understand them will be of paramount importance. Coordination
between statistical agencies and policymaking institutions helps us achieve our shared
goal-a better understanding of the economy.
From our own institutional perspective, the Federal Reserve uses a broad array of
government and private-sector data to continually assess the state of the economy,
inform our outlook for economic activity, and evaluate the risks around that outlook in
pursuit of our dual mandate of maximum employment and stable prices. Not
surprisingly, the emergence of nontraditional, high-frequency data was integral to better
understanding the real-time effects of the COVID-19 pandemic and its economic
2
consequences. In the current environment, we employ a wide variety of tools and
techniques to best distill useful signals related to inflation and the labor market from the
vast array of information we have at our disposal. Of course, that includes the standard
econometric toolkit. But we have also been increasingly relying on text analytics and
machine learning.
As a result, this conference is highly relevant for us at the Federal Reserve, and likely
those at other central banks as well. More timely and accurate information and
improved methodological techniques permit Federal Reserve staff to produce better
estimates of the evolving economic outlook, which allows policymakers to make
moreinformed decisions.
It is reassuring to know that the use of these nontraditional data and techniques for
academic research and for policy is no longer in its infancy. The breadth and depth of
studies captured in the agenda display the material inroads of these data and methods
into economic research.
I am pleased to see colleagues and important contributors to this field on the
conference program. Jed Kolko, Julapa Jagtiani, Arthur Turrell, and Hal Varian will
discuss issues related to the opportunities and challenges for government and
privatesector institutions in response to nontraditional data, machine learning, and artificial
intelligence. Similarly, an academic panel with Jesus Fernandez-Villaverde, Sydney
Ludvigson, Stephen Hansen, and Chiara Farronato will discuss how these data and
methods have helped push the research frontier in subjects ranging from
macroeconomic modeling to online markets.
As I noted in recent remarks, there has been a surge in excitement and trepidation
3
about generative AI. The range of social effects from this new technology could be
broad. Jack Clark, one of the co-founders of Anthropic, will soon offer a keynote, and I
am sure we are all looking forward to hearing his insights on the practical applications
of this new AI technology and the potential use cases for economic research and
policymaking. I am especially interested in seeing progress on "explainable AI," which
can bridge the divide between the technical sphere and users. I am looking forward to
any discussions on this area.
These generative AI tools could also have implications closer to home, as they may
influence how we conduct central bank communications. There is a growing literature
that uses natural language processing techniques to discern how communications by
central bankers are perceived by the news media, and, in turn, how that influences
financial markets. The presentations by Christopher Neely, Clara Vega, Xin Zhang, and
Xu Zhang will discuss the state of the art on this subject, which is of material
importance to us.
Social media has also affected the way we consume news and interact with each other.
This medium is playing an increasingly important role in society and influencing
macroeconomic and financial outcomes. As an example, Corbin Fox will present his
work with coauthors tomorrow on the effect of social media on the bank runs that took
place earlier this year.
On the methodological side, machine-learning techniques have also had a profound
effect on how we think about modeling complex macroeconomic outcomes. This
morning, Philippe Goulet Coulombe will discuss how neural networks help model the
volatility of various macroeconomic variables, and Joël Marbet and Yucheng Yang will
discuss how new methods are being used to solve models with heterogeneous agents,
which are crucial to assess the distributional consequences of economic policies.
Advances in natural language processing and machine learning have also improved our
ability to forecast and nowcast a wide range of macroeconomic and financial indicators,
a topic that will also be covered in a few sessions tomorrow.
In closing, I hope you have many fruitful and informative conversations in this room and
during breaks about the usefulness of nontraditional data and new techniques for
macroeconomic analysis. I also encourage you to continue your discussions after the
conference ends and to seek opportunities for joint work so that we can further develop
our understanding of these exciting new tools.
Welcome to the Federal Reserve! I hope you enjoy the conference.
1
The views expressed here are my own and not necessarily those of my colleagues on
the Federal Open Market Committee.
2
See Tomaz Cajner, Laura J. Feiveson, Christopher J. Kurz, and Stacey Tevlin (2022),
"Use of Nontraditional Data," in Wendy Edelberg, Louise Sheiner, and David Wessel,
eds., Recession Remedies: Lessons Learned from the U.S. Economic Policy Response
to COVID-19 (Washington: Brookings Institution Press), pp. 315-46.
3
See Lisa D. Cook (2023), "Generative AI, Productivity, the Labor Market, and Choice
Behavior," speech delivered at the National Bureau of Economic Research Economics
of Artificial Intelligence Conference, Toronto, Canada, September 22.