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Tracking weekly state-level economic conditions

Author

Listed:
  • Christiane Baumeister

    (University of Notre Dame, University of Pretoria, NBER and CEPR)

  • Danilo Leiva-León

    (Banco de España)

  • Eric Sims

    (University of Notre Dame and NBER)

Abstract

In this paper, we develop a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We show that there is considerable heterogeneity in the length, depth, and timing of business cycles across individual states. We assess the role of states in national recessions and propose an aggregate indicator that allows us to gauge the overall weakness of the U.S. economy. We also illustrate the usefulness of these state-level indices for quantifying the main forces contributing to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of federal economic policies like the Paycheck Protection Program.

Suggested Citation

  • Christiane Baumeister & Danilo Leiva-León & Eric Sims, 2021. "Tracking weekly state-level economic conditions," Working Papers 2134, Banco de España.
    • Handle: RePEc:bde:wpaper:2134
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    References listed on IDEAS

    as
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    Cited by:

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    2. Xin Sheng & Rangan Gupta & Wenting Liao & Oguzhan Cepni, 2024. "The Effects of Uncertainty on Economic Conditions across US States: The Role of Climate Risks," Working Papers 202410, University of Pretoria, Department of Economics.
    3. Emanuele Bacchiocchi & Andrea Bastianin & Graziano Moramarco, 2024. "Macroeconomic Spillovers of Weather Shocks across U.S. States," Working Papers 2024.09, Fondazione Eni Enrico Mattei.
    4. Lyu, Yongjian & Zhang, Xinyu & Cao, Jin & Liu, Jiatao & Yang, Mo, 2024. "Quantitative easing and the spillover effects from the crude oil market to other financial markets: Evidence from QE1 to QE3," Journal of International Money and Finance, Elsevier, vol. 140(C).
    5. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    6. Rueben Ellul & Germano Ruisi, 2022. "Nowcasting the Maltese economy with a dynamic factor model," CBM Working Papers WP/02/2022, Central Bank of Malta.

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    More about this item

    Keywords

    local economic conditions; government policies; weekly indicators; state economies; cross-state heterogeneity; mixed-frequency dynamic factor model; economic weakness index; Markov-switching; recession probabilities;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions

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