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Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions

In: Defining the Spatial Scale in Modern Regional Analysis

Author

Listed:
  • Matías Mayor

    (University of Oviedo)

  • Roberto Patuelli

    (University of Bologna)

Abstract

Forecasting economic values in administrative units provides very important information for political, institutional and economic agents for their respective planning processes. A crucial stage is the choice of the econometric method to obtain these future values taking into account the diversity and complexity of real economy. Two aspects may be considered when choosing an econometric specification. Firstly, disparities in economic development and welfare within countries (i.e., at the regional level) are often bigger than between countries (Elhorst 1995; Taylor and Bradley 1997; Ertur and Le Gallo 2003; Patuelli 2007; see, for example, the cases of Germany and Spain), and they often show typical geographical/spatial structures. Secondly, with regard to regional unemployment disparities, policy makers need, in order to correctly target their actions and policies, to understand two aspects of such disparities: (a) the determinants of ‘equilibrium’ unemployment and its variation; and, (b) the region-specific and the cross-regional dynamics of unemployment. On the one hand, the need for an explicit consideration of the existence of spatial interdependence in econometric models, which is consistent with regional science theories asserting the importance of spatial linkages in local economic processes, led to what is nowadays quite a large literature of empirical papers. On the other hand, the temporal perspective of the problem has attracted less attention in spatial models, but should be considered jointly.

Suggested Citation

  • Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Advances in Spatial Science, in: Esteban Fernández Vázquez & Fernando Rubiera Morollón (ed.), Defining the Spatial Scale in Modern Regional Analysis, edition 127, chapter 0, pages 173-192, Springer.
  • Handle: RePEc:spr:adspcp:978-3-642-31994-5_9
    DOI: 10.1007/978-3-642-31994-5_9
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

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    2. Xu Xiaojie, 2018. "Using Local Information to Improve Short-Run Corn Price Forecasts," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 16(1), pages 1-15, January.
    3. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    4. Roberto Patuelli & Matías Mayor, 2014. "Introduction," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 191-193.
    5. Anna Gloria Billé & Alessio Tomelleri & Francesco Ravazzolo, 2023. "Forecasting regional GDPs: a comparison with spatial dynamic panel data models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(4), pages 530-551, October.
    6. Schanne, Norbert, 2015. "A Global Vector Autoregression (GVAR) model for regional labour markets and its forecasting performance with leading indicators in Germany," IAB-Discussion Paper 201513, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    7. Al Mamun, Md & Sohag, Kazi & Hassan, M. Kabir, 2017. "Governance, resources and growth," Economic Modelling, Elsevier, vol. 63(C), pages 238-261.
    8. Lucian Liviu ALBU & Carlos MatéJIMÉNEZ & Mihaela SIMIONESCU, 2015. "The Assessment of Some Macroeconomic Forecasts for Spain using Aggregated Accuracy Indicators," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 30-47, June.
    9. Semerikova, Elena & Demidova, Olga, 2016. "Using spatial econometric models for regional unemployment forecasting," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 29-51.
    10. Yang, Yang & Zhang, Honglei, 2019. "Spatial-temporal forecasting of tourism demand," Annals of Tourism Research, Elsevier, vol. 75(C), pages 106-119.
    11. Alharbi, Samar S. & Al Mamun, Md & Boubaker, Sabri & Rizvi, Syed Kumail Abbas, 2023. "Green finance and renewable energy: A worldwide evidence," Energy Economics, Elsevier, vol. 118(C).
    12. Schanne, Norbert, 2012. "The formation of experts' expectations on labour markets : do they run with the pack?," IAB-Discussion Paper 201225, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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    More about this item

    Keywords

    Mean Square Error; Unemployment Rate; Spatial Autocorrelation; Forecast Error; Mean Absolute Percentage Error;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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