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Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances
Open accessAbstract
This study develops a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first generalize the GMM estimator suggested in Kelejian and Prucha (1998,1999) for the spatial autoregressive parameter in the disturbance process. We also define IV estimators for the regression parameters of the model and give results concerning the joint asymptotic distribution of those estimators and the GMM estimator. Much of the theory is kept general to cover a wide range of settings.
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Cites
Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data
Driscoll, J. C., & Kraay, A. C. (1998) · Review of Economics and Statistics · doi:10.1162/003465398557825 · Cited by 6,865
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Halleck Vega, S., & Elhorst, J. P. (2015) · Journal of Regional Science · doi:10.1111/jors.12188 · Cited by 649
Metadata and provenance for spatial analysis: the case of spatial weights
Anselin, L., Rey, S. J., & Li, W. (2014) · International Journal of Geographical Information Science · doi:10.1080/13658816.2014.917313 · Cited by 42
Addiction, Thick Market Externalities, and the Persistence of the Opioid Epidemic
Cutler, D., & Donahoe, J. T. (2024) · doi:10.3386/w32055 · Cited by 13
Metadata
- Authors
- Kelejian, H. H., & Prucha, I. R.
- Venue
- Journal of Econometrics
- Publisher
- Elsevier BV
- Published
- 2010-07
- Volume
- 157
- Issue
- 1
- Pages
- 53-67
- ISSN
- 0304-4076 (ISSN-L)
- OpenAlex
- W3121675824
- Cited by
- 1,072
- References
- 1
- FWCI
- 95.42
- Type
- journal article
- Language
- en
- Open access
- green
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- Kelejian, H. H., & Prucha, I. R. (2010). Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2009.10.025
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
