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Unit Root in Unemployment - New Evidence from Nonparametric Tests

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Abstract

We apply range unit-root tests to OECD unemployment rates and compare the results to conventional tests. By simulations, we find that unemployment is represented adequately by a new nonlinear transformation of a serially-correlated I(1) process.

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  • Jürgen Holl & Robert M. Kunst, 2009. "Unit Root in Unemployment - New Evidence from Nonparametric Tests," Vienna Economics Papers vie0915, University of Vienna, Department of Economics.
    • Handle: RePEc:vie:viennp:vie0915
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    1. C. W. J. Granger & Jeff Hallman, 1991. "Nonlinear Transformations Of Integrated Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(3), pages 207-224, May.
    2. Schwert, G William, 2002. "Tests for Unit Roots: A Monte Carlo Investigation," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 5-17, January.
    3. Magnus Gustavsson & Par Osterholm, 2006. "Hysteresis and non-linearities in unemployment rates," Applied Economics Letters, Taylor & Francis Journals, vol. 13(9), pages 545-548.
    4. Felipe Aparicio & Alvaro Escribano & Ana E. Sipols, 2006. "Range Unit‐Root (RUR) Tests: Robust against Nonlinearities, Error Distributions, Structural Breaks and Outliers," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(4), pages 545-576, July.
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    Cited by:

    1. Luis A. Gil-Alana & Antonio Moreno & Seonghoon Cho, 2012. "The Deaton paradox in a long memory context with structural breaks," Applied Economics, Taylor & Francis Journals, vol. 44(25), pages 3309-3322, September.
    2. Monge, Manuel, 2021. "U.S. historical initial jobless claims. Is it different with the coronavirus crisis? A fractional integration analysis," International Economics, Elsevier, vol. 167(C), pages 88-95.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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