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Markov switching in disaggregate unemployment rates

  • Marcelle Chauvet
  • Chinhui Juhn
  • Simon Potter

We develop a dynamic factor model with Markov switching to examine secular and business cycle fluctuations in U.S. unemployment rates. We extract the common dynamics among unemployment rates disaggregated for seven age groups. The framework allows analysis of the contribution of demographic factors to secular changes in unemployment rates. In addition, it allows examination of the separate contribution of changes due to asymmetric business cycle fluctuations. We find strong evidence in favor of the common factor and of the switching between high and low unemployment rate regimes. We also find that demographic adjustments can account for a great deal of the secular change in the unemployment rate, particularly the abrupt increase in the 1970s and 1980s and the subsequent decrease.

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Paper provided by Federal Reserve Bank of New York in its series Staff Reports with number 132.

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Date of creation: 2001
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Handle: RePEc:fip:fednsr:132
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  1. Vredin, Anders & Warne, Anders, 2000. "Unemployment and Inflation Regimes," Working Paper Series 107, Sveriges Riksbank (Central Bank of Sweden).
  2. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
  3. Kim, C-J., 1991. "Dynamic Linear Models with Markov-Switching," Papers 91-8, York (Canada) - Department of Economics.
  4. Chinhui Juhn & Kevin M. Murphy & Robert H. Topel, 1991. "Why Has the Natural Rate of Unemployment Increased over Time?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 22(2), pages 75-142.
  5. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
  6. John Geweke, 1998. "Using simulation methods for Bayesian econometric models: inference, development, and communication," Staff Report 249, Federal Reserve Bank of Minneapolis.
  7. Boldin, Michael D, 1994. "Dating Turning Points in the Business Cycle," The Journal of Business, University of Chicago Press, vol. 67(1), pages 97-131, January.
  8. repec:dgr:kubcen:199965 is not listed on IDEAS
  9. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-28, April.
  10. Geweke, John, 1986. "Exact Inference in the Inequality Constrained Normal Linear Regression Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(2), pages 127-41, April.
  11. Robert J. Gordon, 1981. "Inflation, Flexible Exchange Rates, and the Natural Rate of Unemployment," NBER Working Papers 0708, National Bureau of Economic Research, Inc.
  12. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
  13. Skalin, Joakim & Ter svirta, Timo, 2002. "Modeling Asymmetries And Moving Equilibria In Unemployment Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 6(02), pages 202-241, April.
  14. 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.
  15. Koop, Gary & Potter, Simon M., 1998. "Bayes factors and nonlinearity: Evidence from economic time series1," Journal of Econometrics, Elsevier, vol. 88(2), pages 251-281, November.
  16. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649 Elsevier.
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