Population distribution over time: modelling local spatial dependence with a CAR process

Ilenia Epifani, Chiara Ghiringhelli and Rosella Nicolini

Spatial Economic Analysis, 2020, vol. 15, issue 2, 120-144

Abstract: The effectiveness of local spatial dependence in shaping the population density distribution is investigated. Individual location preferences are modelled by considering the status-related features of a given spatial unit and its neighbours as well as local random spatial dependence. The novelty is framing such a dependence through conditionally autoregressive (CAR) census random effects that are added to a spatially lagged explanatory variable X (SLX) setting. The results not only confirm that controlling for the spatial dimension is relevant but also indicate that local spatial dependence warrants consideration when determining the population distribution of recent decades. In this respect, the framework turns out to be useful for the analysis of microdata in which individual relationships (in a same spatial unit) enforce local spatial dependence.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/17421772.2020.1708442 (text/html)
Access to full text is restricted to subscribers.

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:taf:specan:v:15:y:2020:i:2:p:120-144

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RSEA20

DOI: 10.1080/17421772.2020.1708442

Access Statistics for this article

Spatial Economic Analysis is currently edited by Bernie Fingleton and Danilo Igliori

More articles in Spatial Economic Analysis from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().