An eigenvector spatial filtering contribution to short range regional population forecasting

Daniel A. Griffith and Yongwan Chun

Economics and Business Letters, 2014, vol. 3, issue 4, 208-217

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.

Date: 2014
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://reunido.uniovi.es/index.php/EBL/article/view/10418 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ove:journl:aid:10418

Access Statistics for this article

Economics and Business Letters is currently edited by Francisco J. Delgado

More articles in Economics and Business Letters from Oviedo University Press Contact information at EDIRC.
Bibliographic data for series maintained by Francisco J. Delgado ().