Testing Spatial Dependence in Spatial Models with Endogenous Weights Matrices

Anil K. Bera, Osman Doǧan*, Süleyman Taşplnar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In this study, we propose simple test statistics for identifying the source of spatial dependence in spatial autoregressive models with endogenous weights matrices. Elements of the weights matrices are modelled in such a way that endogenity arises when the unobserved factors that affect elements of the weights matrices are correlated with the unobserved factors in the outcome equation. The proposed test statistics are robust to the presence of endogeneity in the weights and can be used to detect spatial dependence in the dependent variable and/or the disturbance terms. The robust test statistics are easy to calculate as computationally simple estimations are needed for their calculations. Our Monte Carlo results indicate that these tests have good size and power properties in finite samples. We also provide an empirical illustration to demonstrate the usefulness of the robust tests in identifying the source of spatial dependence.

Original languageEnglish
Article number20170015
JournalJournal of Econometric Methods
Volume8
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 Walter de Gruyter GmbH, Berlin/Boston 2019.

Funding

This research was supported, in part, by a grant of computer time from the City University of New York High Performance Computing Center under NSF Grants CNS-0855217 and CNS-0958379.

FundersFunder number
City University of New York High Performance Computing Center
National Science FoundationCNS-0958379, CNS-0855217

    Keywords

    • endogenous spatial weights matrix
    • inference
    • Lagrange multiplier test
    • LM test
    • parametric misspecification
    • Rao's score test
    • robust LM test
    • SARAR model
    • specification testing

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