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A Spatial-Dependence Continuous-Time Model for Regional Unemployment in Germany

Author

Listed:
  • Johan H. L. Oud

    (Radboud University Nijmegen, The Netherlands)

  • Henk Folmer

    (University of Groningen, The Netherlands; University of Wageningen, The Netherlands)

  • Roberto Patuelli

    (University of Lugano, Switzerland; The Rimini Centre for Economic Analysis, Italy)

  • Peter Nijkamp

    (VU University Amsterdam, The Netherlands)

Abstract

This paper analyzes patterns of regional labour market development in Germany over the period 2000-2003 by means of a spatial-dependence continuous-time model. (Spatial) panel data are routinely modelled in discrete time. However, there are compelling arguments for continuous time modelling of (spatial) panel data. Particularly, most social processes evolve in continuous time such that analysis in discrete time is an oversimplification, gives a distorted representation of reality and leads to misinterpretation of estimation results. The most compelling reason for continuous time modelling is that, in contrast to discrete time modelling, it allows for adequate modelling of dynamic adjustment processes (see, for example, Special Issue 62:1, 2008, of Statistica Neerlandica). We introduce spatial dependence in a continuous time modelling framework and apply the unified framework to regional labour market development in Germany. The empirical results show substantial autoregressive effects for unemployment and population development, as well as a negative effect of unemployment development on population development. The reverse effect is not significant. Neither are the effects of the development of regional average wages and of the manufacturing sector on the development of unemployment and population.

Suggested Citation

  • Johan H. L. Oud & Henk Folmer & Roberto Patuelli & Peter Nijkamp, 2008. "A Spatial-Dependence Continuous-Time Model for Regional Unemployment in Germany," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 0811, USI Università della Svizzera italiana.
    • Handle: RePEc:lug:wpaper:0811
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    References listed on IDEAS

    as
    1. Bergstrom, A.R., 1984. "Continuous time stochastic models and issues of aggregation over time," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 20, pages 1145-1212, Elsevier.
    2. Kieran P. Donaghy, 2001. "Solution and econometric estimation of spatial dynamic models in continuous space and continuous time," Journal of Geographical Systems, Springer, vol. 3(3), pages 257-270, November.
    3. Bergstrom, A. R., 1988. "The History of Continuous-Time Econometric Models," Econometric Theory, Cambridge University Press, vol. 4(3), pages 365-383, December.
    4. Tönu Puu, 1997. "Mathematical Location and Land Use Theory," Advances in Spatial Science, Springer, number 978-3-662-03439-2, February.
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    Cited by:

    1. Roberto Patuelli & Norbert Schanne & Daniel A. Griffith & Peter Nijkamp, 2012. "Persistence Of Regional Unemployment: Application Of A Spatial Filtering Approach To Local Labor Markets In Germany," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 300-323, May.
    2. Minna-Liina Ojala & Lauri Hooli, 2022. "Development Cooperation as a Knowledge Creation Process: Rhythmanalytical Approach to a Capacity-Building Project in Zanzibar," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 34(1), pages 367-386, February.
    3. George Grekousis, 2018. "Further Widening or Bridging the Gap? A Cross-Regional Study of Unemployment across the EU Amid Economic Crisis," Sustainability, MDPI, vol. 10(6), pages 1-18, May.
    4. Danny Czamanski & Henk Folmer, 2011. "Introduction: some new methods in regional science," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 47(3), pages 493-497, December.

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    More about this item

    Keywords

    Continuous time modelling; structural equation modelling; spatial dependence; panel data; disattenuation; measurement errors; Germany;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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