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High Frequency Data and a Weekly Economic Index during the Pandemic

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Abstract

This paper describes a weekly economic index (WEI) developed to track the rapid economic developments associated with the onset of and policy response to the novel coronavirus in the United States. The WEI, with its ten component series, tracks the overall economy. Comparing the contributions of the WEI’s components in the 2008 and 2020 recessions reveals differences in how the two events played out at a high frequency. During the 2020 collapse and recovery, it provides a benchmark to interpret similarities and differences of novel indicators with shorter samples and/or nonstationary coverage, such as mobility indexes or credit card spending.

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  • Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2020. "High Frequency Data and a Weekly Economic Index during the Pandemic," Staff Reports 954, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:89215
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    1. Raj Chetty & John N Friedman & Michael Stepner & Opportunity Insights Team & Camille Baker & Harvey Barnhard & Matt Bell & Gregory Bruich & Tina Chelidze & Lucas Chu & Westley Cineus & Sebi Devlin-Fol, 2024. "The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 829-889.
    2. Daniel J. Lewis & Karel Mertens & James H. Stock, 2020. "Monitoring Real Activity in Real Time: The Weekly Economic Index," Liberty Street Economics 20200330b, Federal Reserve Bank of New York.
    3. Tyler Atkinson & Jim Dolmas & Christoffer Koch & Evan F. Koenig & Karel Mertens & Anthony Murphy & Kei-Mu Yi, 2020. "Mobility and Engagement Following the SARS-Cov-2 Outbreak," Working Papers 2014, Federal Reserve Bank of Dallas.
    4. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
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    Cited by:

    1. Daniel Ollech & Deutsche Bundesbank, 2023. "Economic analysis using higher-frequency time series: challenges for seasonal adjustment," Empirical Economics, Springer, vol. 64(3), pages 1375-1398, March.
    2. Luciano Campos & Danilo Leiva-León & Steven Zapata- Álvarez, 2022. "Latin American Falls, Rebounds and Tail Risks," Borradores de Economia 1201, Banco de la Republica de Colombia.
    3. Menezes, Flavio & Figer, Vivian & Jardim, Fernanda & Medeiros, Pedro, 2022. "A near real-time economic activity tracker for the Brazilian economy during the COVID-19 pandemic," Economic Modelling, Elsevier, vol. 112(C).
    4. Toledo Wilfredo, 2021. "Covid-19 and Unemployment: Evidence from Puerto Rico Using Bayesian Analyses with High-Frequency Data," Economics and Business, Sciendo, vol. 35(1), pages 174-189, January.
    5. Luciano Campos & Danilo Leiva-León & Steven Zapata, 2022. "Latin American Falls, Rebounds and Tail," Working Papers 145, Red Nacional de Investigadores en Economía (RedNIE).
    6. Cooray, Arusha & Gangopadhyay, Partha & Das, Narasingha, 2023. "Causality between volatility and the weekly economic index during COVID-19: The predictive power of efficient markets and rational expectations," International Review of Financial Analysis, Elsevier, vol. 89(C).
    7. Kakuho Furukawa & Ryohei Hisano & Yukio Minoura & Tomoyuki Yagi, 2022. "A Nowcasting Model of Industrial Production using Alternative Data and Machine Learning Approaches," Bank of Japan Working Paper Series 22-E-16, Bank of Japan.
    8. Donato Ceci & Andrea Silvestrini, 2023. "Nowcasting the state of the Italian economy: The role of financial markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1569-1593, November.
    9. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2024. "Nowcasting Norwegian household consumption with debit card transaction data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1220-1244, November.
    10. Margaret M. Jacobson & Christian Matthes & Todd B. Walker, 2022. "Inflation Measured Every Day Keeps Adverse Responses Away: Temporal Aggregation and Monetary Policy Transmission," Finance and Economics Discussion Series 2022-054, Board of Governors of the Federal Reserve System (U.S.).
    11. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
    12. Furukawa, Kakuho & Hisano, Ryohei & Minoura, Yukio & Yagi, Tomoyuki, 2024. "A nowcasting model of industrial production using alternative data and machine learning approaches," Japan and the World Economy, Elsevier, vol. 71(C).
    13. Rueben Ellul & Germano Ruisi, 2022. "Nowcasting the Maltese economy with a dynamic factor model," CBM Working Papers WP/02/2022, Central Bank of Malta.
    14. Ollech, Daniel, 2021. "Economic analysis using higher frequency time series: Challenges for seasonal adjustment," Discussion Papers 53/2021, Deutsche Bundesbank.

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    More about this item

    Keywords

    weekly economic index; high frequency; measurement of economic activity; forecasting;
    All these keywords.

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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