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Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro‐level Analysis?

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  • Ralf A. Wilke

Abstract

Forecasting labour market flows is important for budgeting and decision‐making in government departments and public administration. Macroeconomic forecasts are normally obtained from time series data. In this article, we follow another approach that uses individual‐level statistical analysis to predict the number of exits out of unemployment insurance claims. We present a comparative study of econometric, actuarial and statistical methodologies that base on different data structures. The results with records of the German unemployment insurance suggest that prediction based on individual‐level statistical duration analysis constitutes an interesting alternative to aggregate data‐based forecasting. In particular, forecasts of up to six months ahead are surprisingly precise and are found to be more precise than considered time series forecasts.

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  • Ralf A. Wilke, 2018. "Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro‐level Analysis?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(4), pages 822-842, August.
    • Handle: RePEc:bla:obuest:v:80:y:2018:i:4:p:822-842
    DOI: 10.1111/obes.12222
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    Cited by:

    1. Mammen, Enno & Martínez-Miranda, María Dolores & Nielsen, Jens Perch & Vogt, Michael, 2021. "Calendar effect and in-sample forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 31-52.

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