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Model Selection Procedures for Network Autocorrelated Disturbances Models

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  • MALCOLM M. DOW

    (Northwestern University)

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.

Suggested Citation

  • Malcolm M. Dow, 1986. "Model Selection Procedures for Network Autocorrelated Disturbances Models," Sociological Methods & Research, , vol. 14(4), pages 403-422, May.
    • Handle: RePEc:sae:somere:v:14:y:1986:i:4:p:403-422
    DOI: 10.1177/0049124186014004003
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