Model Selection Procedures for Network Autocorrelated Disturbances Models

Malcolm M. Dow
Additional contact information
Malcolm M. Dow: Northwestern University

Sociological Methods & Research, 1986, vol. 14, issue 4, 403-422

Abstract: Strategies for model selection within the regression framework typically involve choices among several sometimes competing criteria. In this article, the interrelated criteria of goodness-of-fit and parameter invariance are explored with respect to a class of maximum likelihood network autocorrelation models. A GLS measure of generalized goodness-of-fit, R 2 G , is proposed for these models based on the equivalence of ML and GLS in the exponential family. This R 2 G statistic can be used to test for stability of parameters across various samples or subsamples. A second test of parameter invariance across subsamples is proposed: Schwarz's (1978) information Criterion. An example illustrates how these identification and testing procedures may be jointly used to help select the most adequate model for a given data set.

Date: 1986
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0049124186014004003 (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:sae:somere:v:14:y:1986:i:4:p:403-422

DOI: 10.1177/0049124186014004003

Access Statistics for this article

More articles in Sociological Methods & Research
Bibliographic data for series maintained by SAGE Publications ().