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Forecasting national recessions using state level data

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

Abstract

A large literature studies the information contained in national-level economic indicators, such as financial 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 benefits 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

  • Michael T. Owyang & Jeremy M. Piger & Howard J. Wall, 2012. "Forecasting national recessions using state level data," Working Papers 2012-013, Federal Reserve Bank of St. Louis.
  • Handle: RePEc:fip:fedlwp:2012-013
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    Cited by:

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    2. 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.
    3. Konrad Adler & Christian Grisse, 2017. "Thousands of BEERs: Take your pick," Review of International Economics, Wiley Blackwell, vol. 25(5), pages 1078-1104, November.
    4. Wall, Howard, 2022. "The Great, Greater, and Greatest Recessions of US States," MPRA Paper 112005, University Library of Munich, Germany.
    5. 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.
    6. 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).
    7. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
    8. Kathryn Bokun & Laura E. Jackson & Kevin L. Kliesen & Michael T. Owyang, 2020. "FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications," Working Papers 2020-031, Federal Reserve Bank of St. Louis, revised 01 Aug 2021.
    9. Joseph, Andreas & Kalamara, Eleni & Kapetanios, George & Potjagailo, Galina, 2021. "Forecasting UK inflation bottom up," Bank of England working papers 915, Bank of England.

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    More about this item

    Keywords

    Recessions; Business cycles; Economic conditions;
    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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