An eigenvector spatial filtering contribution to short range regional population forecasting


  • Daniel A. Griffith University of Texas at Dallas
  • Yongwan Chun University of Texas at Dallas



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.

Author Biography

Daniel A. Griffith, University of Texas at Dallas

Geospatial Information Sciences Program

Asbhel Smith Professor


Chun, Y. and Griffith, D. (2013) Spatial Statistics & Geostatistics, SAGE: Thousand Oaks, CA.

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How to Cite

Griffith, D. A., & Chun, Y. (2014). An eigenvector spatial filtering contribution to short range regional population forecasting. Economics and Business Letters, 3(4), 208–217.