Nontraditional Data, Machine Learning, and Natural Language Processing in Macroeconomics

October 1-2, 2019

Federal Reserve Board, Washington, D.C.

About

The confluence of the expanding access to data and the rapid advance of modelling techniques--like those from machine learning--promises new insights into the economy and a larger information set for policymakers. The Federal Reserve Board will host a two-day conference on October 1-2, 2019,at the Wilson Center at 1801 K St., featuring new research on nontraditional data, machine learning, and natural language processing in macroeconomics. The event will highlight the importance of nontraditional (big) data sources and new empirical methods for macroeconomic analysis, with a particular focus on policymaking.

Note that attendance at the conference is by invitation only, and capacity is limited.

Please direct all questions and correspondence, including paper submissions to: big-data-macro-conf@frb.gov.

Organizing Committee:

  • Christopher Kurz, Division of Research and Statistics
  • Ellen Meade, Division of Monetary Affairs
  • Ricardo Correa, Division of International Finance
  • Leland Crane, Division of Research and Statistics
  • Michiel De Pooter, Division of Monetary Affairs

Call for Papers

We invite you to submit empirical and methodological work leveraging new granular data sources or exploring recent analytical developments to analyze the macroeconomy. The first day of the conference will cover "big data" and the emergent tools that researchers and policymakers are using; the second day will focus on text analysis for macroeconomics, regulation, and communication.

Possible Paper Topics:
The conference will accept high-quality academic papers from a wide range of topics, methods, disciplines, and perspectives. Areas of particular interest include, but are not limited to:

  • Research and policy work focused on novel ways of measuring inflation, the labor market, or macroeconomic activity
  • The use of large, granular structured or unstructured data sources to predict or understand the current state of the economy
  • Machine learning directed at macroeconomic analysis
  • Big data topics covering businesses, households, financial markets, labor markets, or fiscal analysis
  • Natural language processing (NLP) for macroeconomics, financial stability, or banking supervision
  • The use of NLP in analyzing central bank communications

Paper Submissions and Conference Invitations:
We invite authors to submit extended abstracts or completed papers to big-data-macro-conf@frb.gov by April 1, 2019. A strong preference will be shown toward completed papers. Extended abstracts should be 2-4 pages in length and include motivation for the research question, methodology, data sources if an empirical study, and a description of expected research findings and policy implications. (Questions may also be directed to this email address.) Submissions should be in PDF format and indicate who the presenting author will be. The organizing committee will review and select submissions for presentation. Authors of accepted papers will be notified by the end of May 2019. Travel funding may be available for presenters. Please note that conference attendance is by invitation only and capacity is limited.

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Last Update: September 11, 2019