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The Propagation of Regional Recessions

Author

Listed:
  • James D. Hamilton

    (University of California, San Diego)

  • Michael T. Owyang

    (Federal Reserve Bank of St. Louis)

Abstract

This paper develops a framework for inferring common Markov-switching components in panel data sets with large cross-section and time series dimensions. We study similarities and differences across U.S. states in the timing of business cycles. We hypothesize that there exists a small number of cluster designations, with individual states in a given cluster sharing certain business cycle characteristics. We find that although oil-producing and agricultural states can sometimes experience a separate recession from the rest of the United States, for the most part, differences across states appear to be a matter of timing, with some states entering recession or recovering before others. © 2012 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • James D. Hamilton & Michael T. Owyang, 2012. "The Propagation of Regional Recessions," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 935-947, November.
    • Handle: RePEc:tpr:restat:v:94:y:2012:i:4:p:935-947
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    1. Owyang, Michael T. & Piger, Jeremy M. & Wall, Howard J. & Wheeler, Christopher H., 2008. "The economic performance of cities: A Markov-switching approach," Journal of Urban Economics, Elsevier, vol. 64(3), pages 538-550, November.
    2. Sims, Christopher A. & Waggoner, Daniel F. & Zha, Tao, 2008. "Methods for inference in large multiple-equation Markov-switching models," Journal of Econometrics, Elsevier, vol. 146(2), pages 255-274, October.
    3. James D. Hamilton, 2005. "What's real about the business cycle?," Review, Federal Reserve Bank of St. Louis, vol. 87(Jul), pages 435-452.
    4. Gerald Carlino & Keith Sill, 2001. "Regional Income Fluctuations: Common Trends And Common Cycles," The Review of Economics and Statistics, MIT Press, vol. 83(3), pages 446-456, August.
    5. Andrew Harvey (ed.), 1994. "Time Series," Books, Edward Elgar Publishing, volume 0, number 599, December.
    6. Geweke, John & Keane, Michael, 2007. "Smoothly mixing regressions," Journal of Econometrics, Elsevier, vol. 138(1), pages 252-290, May.
    7. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, October.
    8. Krueger, Alan B & Burton, John F, Jr, 1990. "The Employers' Costs of Workers' Compensation Insurance: Magnitudes, Determinants, and Public Policy," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 228-240, May.
    9. Theodore M. Crone, 2005. "An Alternative Definition of Economic Regions in the United States Based on Similarities in State Business Cycles," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 617-626, November.
    10. 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.
    11. 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 357-384, March.
    12. Bram van Dijk & Philip Hans Franses & Richard Paap & Dick van Dijk, 2011. "Modelling regional house prices," Applied Economics, Taylor & Francis Journals, vol. 43(17), pages 2097-2110.
    13. Michael A. Kouparitsas, 1999. "Is the United States an optimal currency area?," Chicago Fed Letter, Federal Reserve Bank of Chicago, issue Oct.
    14. Del Negro, Marco, 2002. "Asymmetric shocks among U.S. states," Journal of International Economics, Elsevier, vol. 56(2), pages 273-297, March.
    15. Mark D. Partridge & Dan S. Rickman, 2005. "Regional cyclical asymmetries in an optimal currency area: an analysis using US state data," Oxford Economic Papers, Oxford University Press, vol. 57(3), pages 373-397, July.
    16. Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
    17. Carlino, Gerald A. & DeFina, Robert H., 2004. "How strong is co-movement in employment over the business cycle? Evidence from state/sector data," Journal of Urban Economics, Elsevier, vol. 55(2), pages 298-315, March.
    18. Forni, Mario & Reichlin, Lucrezia, 2001. "Federal policies and local economies: Europe and the US," European Economic Review, Elsevier, vol. 45(1), pages 109-134, January.
    19. Fruhwirth-Schnatter, Sylvia & Kaufmann, Sylvia, 2008. "Model-Based Clustering of Multiple Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 78-89, January.
    20. Gerald Carlino & Robert Defina, 1998. "The Differential Regional Effects Of Monetary Policy," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 572-587, November.
    21. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
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    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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