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

Author

Listed:
  • Matías Mayor

    (Department of Applied Economics, University of Oviedo, Spain)

  • Roberto Patuelli

    (Department of Economics, Faculty of Economics-Rimini, University of Bologna, Italy; The Rimini Centre for Economic Analysis (RCEA), Italy)

Abstract

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need accurate forecasts of the unemployment evolution in order to design short- or long-run local welfare policies. These predictions should then consider the spatial interrelations and dynamics of regional unemployment. In addition, a number of papers have demonstrated the improvement in the reliability of long-run forecasts when spatial dependence is accounted for. We estimate a heterogeneous coefficients dynamic panel model employing a spatial filter in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment, as well as a spatial vector-autoregressive (SVAR) model. We compare the short-run forecasting performance of these methods, and in particular, we carry out a sensitivity analysis in order to investigate if different number and size of the administrative regions influence their relative forecasting performance. We compute short-run unemployment forecasts in two countries with different administrative territorial divisions and data frequency: Switzerland (26 regions, monthly data for 34 years) and Spain (47 regions, quarterly data for 32 years)

Suggested Citation

  • Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Working Paper series 15_12, Rimini Centre for Economic Analysis, revised Oct 2012.
    • Handle: RePEc:rim:rimwps:15_12
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    References listed on IDEAS

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    Cited by:

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    2. Wozniak Marcin, 2020. "Forecasting the unemployment rate over districts with the use of distinct methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-20, April.
    3. Al Mamun, Md & Sohag, Kazi & Hassan, M. Kabir, 2017. "Governance, resources and growth," Economic Modelling, Elsevier, vol. 63(C), pages 238-261.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Yang, Yang & Zhang, Honglei, 2019. "Spatial-temporal forecasting of tourism demand," Annals of Tourism Research, Elsevier, vol. 75(C), pages 106-119.
    9. 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).
    10. 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].
    11. Roberto Patuelli & Matías Mayor, 2014. "Introduction," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 191-193.
    12. 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].

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    More about this item

    Keywords

    regional forecasts; spatial econometrics; dynamic panel; SVAR;
    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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