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An eigenvector spatial filtering contribution to short range regional population forecasting

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  • Daniel A. Griffith
  • Yongwan Chun

Abstract

Statistical space-time forecasting requires sufficiently large time series data to ensure high quality predictions. The dominance of temporal dependence in empirical space-time data emphasizes the importance of a lengthy time sequence. However, regional space-time data often have a relative small temporal sample size, increasing chances that regional forecasts might result in unreliable predictions. This paper proposes a method to improve regional forecasts by incorporating spatial autocorrelation in a generalized linear mixed model framework coupled with eigenvector spatial filtering. This methodology is illustrated with an application of regional population forecasts for South Korea.

Suggested Citation

  • Daniel A. Griffith & Yongwan Chun, 2014. "An eigenvector spatial filtering contribution to short range regional population forecasting," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 208-217.
    • Handle: RePEc:ove:journl:aid:10418
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    File URL: https://reunido.uniovi.es/index.php/EBL/article/view/10418
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    Cited by:

    1. Akinola Ezekiel Morakinyo & Mabutho Sibanda, 2016. "The Determinants of Non-Performing Loans in the MINT Economies," Journal of Economics and Behavioral Studies, AMH International, vol. 8(5), pages 39-55.
    2. Yang, Yang & Zhang, Honglei, 2019. "Spatial-temporal forecasting of tourism demand," Annals of Tourism Research, Elsevier, vol. 75(C), pages 106-119.

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