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Continuous-Time Modelling with Spatial Dependence

Author

Listed:
  • Johan H.L. Oud

    (Radboud University Nijmegen)

  • Henk Folmer

    (University of Groningen)

  • Roberto Patuelli

    (University of Lugano)

  • Peter Nijkamp

    (VU University Amsterdam)

Abstract

This discussion paper led to an article in Geographical Analysis (2012). Volume 44, issue 1, pages 29-46. (Spatial) panel data are routinely modelled in discrete time (DT). However, there are compelling arguments for continuous time (CT) modelling of (spatial) panel data. Particularly, most social processes evolve in CT, so that statistical analysis in DT is an oversimplification, gives an incomplete representation of reality and may lead to misinterpretation of estimation results. The most compelling reason for a CT approach is that, in contrast to DT modelling, it allows adequate modelling of dynamic adjustment processes. The paper introduces spatial dependence in a CT modelling framework. We propose a nonlinear Structural Equation Model (SEM) with latent variables for estimation of the Exact Discrete Model (EDM), which links the CT model parameters to the DT observations. The use of a SEM with latent variables makes it possible to take measurement errors in the variables into account, leading to a reduction of attenuation bias (i.e., disattenuation). The SE M-CT model with spatial dependence developed here is the first dynamic structural equation model with spatial dependence. The spatial econometric SEM-CT framework is illustrated on the basis of a simple regional labour market model for Germany made up of the endogenous state variables unemployment change and population change and of the exogenous input variables change in regional average wage and change in the structure of the manufacturing sector.

Suggested Citation

  • Johan H.L. Oud & Henk Folmer & Roberto Patuelli & Peter Nijkamp, 2011. "Continuous-Time Modelling with Spatial Dependence," Tinbergen Institute Discussion Papers 11-117/3, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20110117
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    References listed on IDEAS

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    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.
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    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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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    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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