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Forecasting national recessions using state-level data

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  • Owyang, Michael T.
  • Piger, Jeremy
  • Wall, Howard J.

Abstract

A large literature studies the information contained in national-level economic indicators, such as nancial and aggregate economic activity variables, for forecasting U.S. business cycle phases (expansions and recessions.) In this paper, we investigate whether there is additional information regarding business cycle phases contained in subnational measures of economic activity. Using a probit model to predict the NBER expansion and recession classification, we assess the forecasting benets of adding state-level employment growth to a common list of national-level predictors. As state-level data adds a large number of variables to the model, we employ a Bayesian model averaging procedure to construct forecasts. Based on a variety of forecast evaluation metrics, we find that including state-level employment growth substantially improves short-horizon forecasts of the business cycle phase. The gains in forecast accuracy are concentrated during months of national recession. Posterior inclusion probabilities indicate substantial uncertainty regarding which states belong in the model, highlighting the importance of the Bayesian model averaging approach.

Suggested Citation

  • Owyang, Michael T. & Piger, Jeremy & Wall, Howard J., 2012. "Forecasting national recessions using state-level data," MPRA Paper 39168, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:39168
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    References listed on IDEAS

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    Cited by:

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    2. Wall, Howard, 2023. "The Great, Greater, and Greatest Recessions of US States," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 53(1), January.
    3. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    4. Fossati Sebastian, 2016. "Dating US business cycles with macro factors," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(5), pages 529-547, December.
    5. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    6. Cepni, Oguzhan & Christou, Christina & Gupta, Rangan, 2023. "Forecasting national recessions of the United States with state-level climate risks: Evidence from model averaging in Markov-switching models," Economics Letters, Elsevier, vol. 227(C).
    7. Bokun, Kathryn O. & Jackson, Laura E. & Kliesen, Kevin L. & Owyang, Michael T., 2023. "FRED-SD: A real-time database for state-level data with forecasting applications," International Journal of Forecasting, Elsevier, vol. 39(1), pages 279-297.
    8. Christiansen, Charlotte & Eriksen, Jonas N. & Møller, Stig V., 2019. "Negative house price co-movements and US recessions," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 382-394.
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    10. Di Caro, Paolo, 2014. "Regional recessions and recoveries in theory and practice: a resilience-based overview," MPRA Paper 60300, University Library of Munich, Germany.
    11. Dey, Asim K. & Hoque, G.M. Toufiqul & Das, Kumer P. & Panovska, Irina, 2022. "Impacts of COVID-19 local spread and Google search trend on the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    12. Konrad Adler & Christian Grisse, 2017. "Thousands of BEERs: Take your pick," Review of International Economics, Wiley Blackwell, vol. 25(5), pages 1078-1104, November.
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    More about this item

    Keywords

    turning points; probit; covariate selection;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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