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.

Descargas

Los datos de descargas todavía no están disponibles.

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

Licencia

The works published in this journal are subject to the following terms:

1. Oviedo University Press (the publisher) retains the property rights (copyright) of published works, and encourages and enables the reuse of the same under the license specified in paragraph 2.

© Ediuno. Ediciones de la Universidad de Oviedo / Oviedo University Press

2. The works are published in the online edition of the journal under a Creative Commons Attribution-Non Commercial-Non Derives 3.0 Spain (legal text). You can copy, use, distribute, transmit and publicly display, provided that: i) you cite the author and the original source of publication (journal, publisher and URL of the work), ii) they are not used for commercial purposes, iii) mentions the existence and specifications of this license.

3. Conditions of self-archiving. The author can archive the post-print version of the article (publisher’s version) on the author’s personal website and/or on the web of  the institution where he belong, including a link to the page of the journal and putting the way of citation of the work. Economics and Business Letters and its URL https://reunido.uniovi.es/index.php/EBL/index are the only authorized source for correctly giving the reference of the publisher’s version in every mention of the article.