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Forecasting with a mismatch-enhanced labor market matching function

Author

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  • Hutter, Christian

    () (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])

  • Weber, Enzo

    () (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])

Abstract

"This paper investigates the role of mismatch between job seekers and job openings for the forecasting performance of a labor market matching function. In theory, higher mismatch lowers matching efficiency which increases the risk that the vacancies cannot be filled within the usual period of time. We investigate whether and to what extent forecasts of German job findings can be improved by a mismatch-enhanced labor market matching function. For this purpose, we construct so-called mismatch indicators that reflect regional, occupational and qualification-related mismatch on a monthly basis. In pseudo out-of-sample tests that account for the nested model environment, we find that forecasting models enhanced by the mismatch indicator significantly outperform their benchmark counterparts for all forecast horizons ranging between one month and a year. This is especially pronounced in the aftermath of the Great Recession where a low level of mismatch improved the possibility of unemployed to find a job again." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Hutter, Christian & Weber, Enzo, 2014. "Forecasting with a mismatch-enhanced labor market matching function," IAB Discussion Paper 201416, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:201416
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    References listed on IDEAS

    as
    1. Ebrahimy, Ehsan & Shimer, Robert, 2010. "Stock-flow matching," Journal of Economic Theory, Elsevier, vol. 145(4), pages 1325-1353, July.
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    3. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    4. Lilien, David M, 1982. "Sectoral Shifts and Cyclical Unemployment," Journal of Political Economy, University of Chicago Press, vol. 90(4), pages 777-793, August.
    5. Christopher A. Pissarides & Barbara Petrongolo, 2001. "Looking into the Black Box: A Survey of the Matching Function," Journal of Economic Literature, American Economic Association, vol. 39(2), pages 390-431, June.
    6. Jackman, R & Roper, S, 1987. "Structural Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 49(1), pages 9-36, February.
    7. Maria E. Canon & Mingyu Chen & Elise Marifian, 2013. "Labor mismatch in the Great Recession: a review of indexes using recent U.S. data," Review, Federal Reserve Bank of St. Louis, vol. 95(May), pages 237-272.
    8. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
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    12. Sabine Klinger & Enzo Weber, 2016. "Decomposing Beveridge Curve Dynamics By Correlated Unobserved Components," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(6), pages 877-894, December.
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    Cited by:

    1. Stephan, Gesine & Uthmann, Sven, 2014. "Akzeptanz von Vergeltungsmaßnahmen am Arbeitsplatz : Befunde aus einer quasi-experimentellen Untersuchung," IAB Discussion Paper 201427, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

    More about this item

    Keywords

    mismatch; offene Stellen; Arbeitslose; Qualifikationsanforderungen; Qualifikationsmerkmale; Prognoseverfahren; Stellenbesetzung - Dauer; Indikatorenbildung;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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