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Forecasting Regional Labor Market Developments under Spatial Autocorrelation

Author

Listed:
  • Simonetta Longhi

    (Institute for Social and Economic Research, University of Essex, Colchester, UK, slonghi@essex.ac.uk)

  • Peter Nijkamp

    (Department of Spatial Economics, Free University Amsterdam, the Netherlands, pnijkamp@feweb.vu.nl)

Abstract

Because of heterogeneity across regions, economic policy measures are increasingly targeted at the regional level and, therefore, require regional forecasts. The data available to compute regional forecasts are usually a pseudo panel of a limited number of observations over time and a large number of regions strongly interacting with each other. Traditional time-series techniques applied to distinct time series of regional data are probably a suboptimal forecasting strategy. Although both linear and nonlinear models have been applied and evaluated to forecast socioeconomic variables, spatial interactions among regions are often ignored. This article evaluates the ability of spatial error and spatial lag models to correct for misspecifications due to neglected spatial autocorrelation in the data. The empirical application on short-term forecasts of employment in 326 West German regions shows that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of nonspatial models.

Suggested Citation

  • Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
  • Handle: RePEc:sae:inrsre:v:30:y:2007:i:2:p:100-119
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65, January.
    2. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2018. "A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors," MPRA Paper 86371, University Library of Munich, Germany.
    3. Kwon, Sanguk & Cho, Seong-Hoon & Roberts, Roland Keith & Kim, Taeyoung & Yu, T. Edward, 2015. "Effects of changes in electricity price on electricity demand and resulting effects on manufacturing output," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196850, Southern Agricultural Economics Association.
    4. 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.
    5. Eric Girardin & Konstantin A. Kholodilin, 2011. "How helpful are spatial effects in forecasting the growth of Chinese provinces?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 622-643, November.
    6. Lee, Lung-fei & Yu, Jihai, 2015. "Estimation of fixed effects panel regression models with separable and nonseparable space–time filters," Journal of Econometrics, Elsevier, vol. 184(1), pages 174-192.
    7. Semerikova, Elena & Demidova, Olga, 2016. "Using spatial econometric models for regional unemployment forecasting," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 43, pages 29-51.
    8. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, Elsevier.
    9. Robert Lehmann & Klaus Wohlrabe, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, Verein für Socialpolitik, vol. 16(2), pages 226-254, May.
    10. Li Dong & Le Canh, 2010. "Nonlinearity and Spatial Lag Dependence: Tests Based on Double-Length Regressions," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-18, June.
    11. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    12. Kwon, Sanguk & Cho, Seong-Hoon & Roberts, Roland K. & Kim, Hyun Jae & Park, KiHyun & Edward Yu, Tun-Hsiang, 2016. "Short-run and the long-run effects of electricity price on electricity intensity across regions," Applied Energy, Elsevier, vol. 172(C), pages 372-382.
    13. Ana Angulo & Jesús Mur & Javier Trivez, 2014. "Measure of the resilience to Spanish economic crisis: the role of specialization," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 263-275.
    14. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 0902, USI Università della Svizzera italiana.
    15. Yang, Yuan & Zhang, Junjie & Wang, Can, 2014. "Is China on Track to Comply with Its 2020 Copenhagen Carbon Intensity Commitment?," University of California at San Diego, Economics Working Paper Series qt1r5251g8, Department of Economics, UC San Diego.
    16. Baltagi, Badi H. & Pirotte, Alain, 2014. "Prediction in a spatial nested error components panel data model," International Journal of Forecasting, Elsevier, vol. 30(3), pages 407-414.
    17. Le, Canh Quang & Li, Dong, 2008. "Double-Length Regression tests for testing functional forms and spatial error dependence," Economics Letters, Elsevier, vol. 101(3), pages 253-257, December.
    18. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2018. "A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors," IZA Discussion Papers 11587, Institute for the Study of Labor (IZA).
    19. 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].
    20. repec:ura:ecregj:v:1:y:2017:i:2:p:410-421 is not listed on IDEAS
    21. Kwon, Sanguk & Cho, Seong-Hoon & Roberts, Roland K. & Kim, Hyun Jae & Park, Kihyun & Edward Yu, T., 2016. "Effects of electricity-price policy on electricity demand and manufacturing output," Energy, Elsevier, vol. 102(C), pages 324-334.

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