Richard H Clarida: Introductory remarks -"Nontraditional Data,
Machine Learning, and Natural Language Processing in
Macroeconomics"
Introductory remarks by Mr Richard H Clarida, Vice Chairman of the Board of Governors of the
Federal Reserve System, at "Nontraditional Data, Machine Learning, and Natural Language
Processing in Macroeconomics", a research conference sponsored by the Federal Reserve
Board, Washington DC, 1 October 2019.
* * *
Good morning, and welcome to the Federal Reserve Board's research conference
"Nontraditional Data, Machine Learning, and Natural Language Processing in Macroeconomics."
Here at the Fed, we are continually assessing the current state of the economy, updating our
outlook for economic activity, and estimating the risks around that outlook. In this environment,
we assess a broad array of government and private-sector data to determine what they imply for
the achievement of the Fed's statutory goals of maximum employment and price stability. As a
result, this conference and the range of topics on your agenda for today and tomorrow are highly
relevant for us. More timely and accurate information sourced from nontraditional data and the
use of new techniques should permit Board staff economists to make better estimates of the
evolving news and what it implies for the economic outlook and allow policymakers to make
better-informed decisions.
Over these next two days, you will hear about the use of new tools and nontraditional data
sources and what they say for the assessment of inflation and the labor market; about the use of
new methods for forecasting; and about extracting information from text and using textual
analysis to evaluate regulatory complexity and understand central bank communications. You no
doubt will have many conversations in this room and during breaks about the usefulness of big
data and new techniques for macroeconomic analysis. I am pleased to see some former
colleagues and important contributors to macroeconomics and measurement on your
conference program, such as Erica Groshen; Ron Jarmin; Matthew Shapiro; Hal Varian; and
David Wilcox, who recently left the Fed for other pastures.
I would also like to acknowledge the diversity this conference offers. This conference is
interdisciplinary, bringing together people from many different fields of study-economists,
computer scientists, and statisticians-as well as people from many different types of
institutions, including universities, central bank research departments, statistical agencies, and
the private sector. We all stand to benefit from work across disciplines, and the connections
forged at conferences such as this one can be highly fruitful.
To a large extent, the use of nontraditional data, machine learning, and natural language
processing in macroeconomics and for policy is only just in its infancy. In many cases, we are
unsure of the efficacy or benefits of these approaches. Coordination between statistical agencies
and policymaking institutions will help us achieve our shared goal-a better understanding of the
economy. To this end, the discussions on the use of big data and new techniques for central
banking and on the possibilities for cooperation between private companies and government
agencies should be particularly helpful. Moreover, I strongly 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 big data and textual analysis.
Now I would like to invite the participants in the session on alternative data on inflation and the
labor market to come to the podium. Welcome to the Federal Reserve, and I wish you a
successful two days.