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A Time Series Analysis Of Disaggregate U.S. Unemployment

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  • HOTCHKISS, J.L.

Abstract

This paper addresses an issue that has been neglected in the vast array of literature focusing on the determination of and differences in regional unemployment rates. The issue of whether causal relationships exist between disaggregated unemployment rates is considered. A multivariate vector autoregression model is estimated separately for unemployment rates of ten states and nine regions. The resulting impulse response functions indicate that significant causal relationships exist between unemployment rates of the states and regions analyzed. The results suggest that a mechanism allowing for causal relationships between disaggregate unemployment rates should be incorporated into future analyses.
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Suggested Citation

  • Hotchkiss, J.L., 1988. "A Time Series Analysis Of Disaggregate U.S. Unemployment," Papers 412, Cornell - Department of Economics.
    • Handle: RePEc:fth:cornel:412
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    Cited by:

    1. Ewing, Bradley T. & Levernier, William, 2000. "An Analysis of Rural-Urban Differences in Average Family Income: An Application of the Oaxaca and Cotton-Neumark Decomposition Technique," The Review of Regional Studies, Southern Regional Science Association, vol. 30(3), pages 299-314, Winter.
    2. Julie L. Hotchkiss & Robert E. Moore, 2022. "Some Like it Hot: Assessing Longer-Term Labor Market Benefits from a High-Pressure Economy," International Journal of Central Banking, International Journal of Central Banking, vol. 18(2), pages 193-243, June.
    3. James Payne & Bradley Ewing & Erik George, 1999. "Time series dynamics of US State unemployment rates," Applied Economics, Taylor & Francis Journals, vol. 31(11), pages 1503-1510.

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    Keywords

    unemployment ; macroeconomics;

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