An eigenvector spatial filtering contribution to short range regional population forecasting

Autores/as

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

DOI:

https://doi.org/10.17811/ebl.3.4.2014.208-217

Resumen

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.

Biografía del autor/a

Daniel A. Griffith, University of Texas at Dallas

Geospatial Information Sciences Program

Asbhel Smith Professor

Citas

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

Goodman, L. (1962) The variance of the product of K random variables, J. of the American Statistical Association, 57, 54-60.

Griffith, D. (2012) Space, time, and space-time eigenvector filter specifications that account for autocorrelation. Estadística Española, 54 (177), 7-34.

Griffith, D. (2013) Estimating missing data values for georeferenced Poisson counts, Geographical Analysis, 45, 259-284.

Griffith, D. and Paelinck, J. (2009) Specifying a joint space- and time-lag using a bivariate Poisson distribution, J. of Geographical Systems, 11, 23-36.

McCullogch, C., Searle, S. and Neuhaus, J. (2008) Generalized, Linear, and Mixed Models, 2nd ed., Wiley: New York.

Descargas

Publicado

2014-12-30

Cómo citar

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. https://doi.org/10.17811/ebl.3.4.2014.208-217

Número

Vol. 3 Núm. 4 (2014): December - Special Issue Advances in Regional Forecasting

Sección

Special Section

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