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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.>

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Bibliographic Info

Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2012-013.

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Date of creation: 2012
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Handle: RePEc:fip:fedlwp:2012-013

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Keywords: Recessions ; Business cycles ; Economic conditions;

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  1. Thomas B. King & Andrew T. Levin & Roberto Perli, 2007. "Financial market perceptions of recession risk," Finance and Economics Discussion Series 2007-57, Board of Governors of the Federal Reserve System (U.S.).
  2. Estrella, Arturo, 1998. "A New Measure of Fit for Equations with Dichotomous Dependent Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 198-205, April.
  3. James D. Hamilton & Michael T. Owyang, 2011. "The Propagation of Regional Recessions," NBER Working Papers 16657, National Bureau of Economic Research, Inc.
  4. Hamilton, James D., 2011. "Calling recessions in real time," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1006-1026, October.
  5. Hernandez-Murillo, Ruben & Owyang, Michael T., 2006. "The information content of regional employment data for forecasting aggregate conditions," Economics Letters, Elsevier, vol. 90(3), pages 335-339, March.
  6. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2005. "Business Cycle Phases in U.S. States," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 604-616, November.
  7. Wesley Clair Mitchell, 1927. "Business Cycles: The Problem and Its Setting," NBER Books, National Bureau of Economic Research, Inc, number mitc27-1, May.
  8. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  9. Marcelle Chauvet & Jeremy M. Piger, 2005. "A comparison of the real-time performance of business cycle dating methods," Working Papers 2005-021, Federal Reserve Bank of St. Louis.
  10. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, May.
  11. Wesley Clair Mitchell, 1927. "Introductory pages to "Business Cycles: The Problem and Its Setting"," NBER Chapters, in: Business Cycles: The Problem and Its Setting, pages -23 National Bureau of Economic Research, Inc.
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