Akaike information criterion in choosing the optimal k-nearest neighbours of the spatial weight matrix

Maria Kubara and Katarzyna Kopczewska

Spatial Economic Analysis, 2024, vol. 19, issue 1, 73-91

Abstract: We use the Akaike information criterion (AIC) to assess the quality of non-nested spatial econometric models with a different number of nearest neighbours (knn) included in the spatial weight matrix W. This is of particular importance in two cases: when estimating the model on geolocated point data without explicit guidance on the optimal value of knn; and when dealing with different spatial patterns and a unique W generalizes diverse spatial structures. By minimizing AIC for a set of competing models, one can find the optimal knn that guarantees the best fit. We show through simulation and empirical analysis that AIC is a non-linear function of knn and reaches its minimum for a given knn. We provide practical evidence that misspecified W may result in a 20% bias of model parameters.

Date: 2024
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/17421772.2023.2176539 (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:19:y:2024:i:1:p:73-91

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

DOI: 10.1080/17421772.2023.2176539

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 ().