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Markov switching in disaggregate unemployment rates

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  • Marcelle Chauvet
  • Chinhui Juhn
  • Simon M. Potter

Abstract

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.

Suggested Citation

  • Marcelle Chauvet & Chinhui Juhn & Simon M. Potter, 2001. "Markov switching in disaggregate unemployment rates," Staff Reports 132, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:132
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    Cited by:

    1. Michael W. McCracken & Joseph T. McGillicuddy & Michael T. Owyang, 2022. "Binary Conditional Forecasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1246-1258, June.
    2. Michael Dueker & Laura E Jackson & Michael T Owyang & Martin Sola, 2023. "A time-varying threshold STAR model with applications," Oxford Open Economics, Oxford University Press, vol. 2, pages 63-98.
    3. José Cancelo, 2007. "Cyclical Asymmetries in Unemployment Rates: International Evidence," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 13(3), pages 334-346, August.
    4. Mili, Mehdi & Sahut, Jean-Michel & Teulon, Frédéric, 2012. "Non linear and asymmetric linkages between real growth in the Euro area and global financial market conditions: New evidence," Economic Modelling, Elsevier, vol. 29(3), pages 734-741.
    5. Hamilton, James D., 2003. "Comment on "A comparison of two business cycle dating methods"," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1691-1693, July.
    6. Maximo Camacho, 2004. "Vector smooth transition regression models for US GDP and the composite index of leading indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 173-196.
    7. Marianna Oliskevych & Iryna Lukianenko, 2020. "European unemployment nonlinear dynamics over the business cycles: Markov switching approach," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 22(4), pages 375-401.
    8. repec:kap:iaecre:v:13:y:2007:i:3:p:334-346 is not listed on IDEAS
    9. Jean-michel Sahut & Medhi Mili & Frédéric Teulon, 2012. "What is the linkage between real growth in the Euro area and global financial market conditions?," Economics Bulletin, AccessEcon, vol. 32(3), pages 2464-2480.

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    More about this item

    Keywords

    Econometric models; Unemployment; Business cycles;
    All these keywords.

    JEL classification:

    • 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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