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Nowcasting Unemployment Insurance Claims in the Time of COVID-19

Author

Listed:
  • William D. Larson

    (Federal Housing Finance Agency)

  • Tara M. Sinclair

    (George Washington University)

Abstract

Near term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. Models including Google Trends are outperformed by alternative models in nearly all periods. Our results suggest that in times of structural change there may be simple approaches to exploit relevant information in the cross sectional dimension to improve forecasts.

Suggested Citation

  • William D. Larson & Tara M. Sinclair, 2020. "Nowcasting Unemployment Insurance Claims in the Time of COVID-19," FHFA Staff Working Papers 20-02, Federal Housing Finance Agency.
  • Handle: RePEc:hfa:wpaper:20-02
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Modelling > Statistical Modelling

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    Cited by:

    1. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    2. Willem Thorbecke, 2020. "The Impact of the COVID-19 Pandemic on the U.S. Economy: Evidence from the Stock Market," JRFM, MDPI, vol. 13(10), pages 1-30, October.
    3. Lin William Cong & Ke Tang & Bing Wang & Jingyuan Wang, 2021. "An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States," Papers 2109.10009, arXiv.org.
    4. Liu, Laura & Moon, Hyungsik Roger & Schorfheide, Frank, 2021. "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, Elsevier, vol. 220(1), pages 2-22.
    5. Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
    6. Danilo Cascaldi-Garcia & Thiago Revil T. Ferreira & Domenico Giannone & Michele Modugno, 2021. "Back to the Present: Learning about the Euro Area through a Now-casting Model," International Finance Discussion Papers 1313, Board of Governors of the Federal Reserve System (U.S.).
    7. Dergiades, Theologos & Milas, Costas & Panagiotidis, Theodore, 2022. "Unemployment claims during COVID-19 and economic support measures in the U.S," Economic Modelling, Elsevier, vol. 113(C).

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    More about this item

    Keywords

    land prices; price gradient; land value taxation; price dynamics;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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