The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Bayesian Estimation of Spatial Autoregressive Models
Abstract
Spatial econometrics has relied extensively on spatial autoregressive models. Anselin (1988) developed a taxonomy of these models using a regression model framework and maximum likelihood estimation methods. A Bayesian approach to estimating these models based on Gibbs sampling is introduced here. It allows for non-constant variance over space taking an unspecified form and outliers in the sample data. In addition, estimates of the non-constant variance at each point in space allow inferences regarding the spatial nature of heteroskedasticity and the position of outliers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Newey, W. K., & West, K. D. (1987) · Econometrica · doi:10.2307/1913610 · Cited by 16,936
A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix
Newey, W., & West, K. (1986) · National Bureau of Economic Research · doi:10.3386/t0055 · Cited by 12,278
Spatial Econometrics: Methods and Models
Anselin, L. (1988) · Studies in Operational Regional Science · doi:10.1007/978-94-015-7799-1 · Cited by 9,274
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
Non-Uniform Random Variate Generation
Devroye, L. (1986) · Springer eBooks · doi:10.1007/978-1-4613-8643-8 · Cited by 4,041
Casella, G., & George, E. I. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475878 · Cited by 2,364
Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling
Gelfand, A. E., Hills, S. E., Racine-Poon, A., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10474968 · Cited by 969
DO SPATIAL EFFECFS REALLY MATTER IN REGRESSION ANALYSIS?
Anselin, L., & Griffith, D. A. (1988) · Papers in Regional Science · doi:10.1111/j.1435-5597.1988.tb01155.x · Cited by 567
Härdle, W. (1991) · Springer Series in Statistics · doi:10.1007/978-1-4612-4432-5 · Cited by 551
Bayesian Analysis of Constrained Parameter and Truncated Data Problems Using Gibbs Sampling
Gelfand, A. E., Smith, A. F. M., & Lee, T.-M. (1992) · Journal of the American Statistical Association · doi:10.1080/01621459.1992.10475235 · Cited by 476
Cited by
Growth, technological interdependence and spatial externalities: theory and evidence
Ertur, C., & Koch, W. (2007) · Journal of Applied Econometrics · doi:10.1002/jae.963 · Cited by 622
The influential factors of urban PM2.5 concentrations in China: a spatial econometric analysis
Hao, Y., & Liu, Y.-M. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2015.05.005 · Cited by 543
Interdependent preferential trade agreement memberships: An empirical analysis
Egger, P., & Larch, M. (2008) · Journal of International Economics · doi:10.1016/j.jinteco.2008.08.003 · Cited by 505
“Theory and Practice of Spatial Econometrics”
(2015) · Spatial Economic Analysis · doi:10.1080/17421772.2015.1062285 · Cited by 481
Metadata
- Authors
- Lesage, J. P.
- Venue
- International Regional Science Review
- Publisher
- SAGE Publications
- Published
- 1997-04
- Volume
- 20
- Issue
- 1-2
- Pages
- 113-129
- ISSN
- 0160-0176 (ISSN-L)
- 1552-6925
- OpenAlex
- W2141340048
- Cited by
- 339
- References
- 17
- FWCI
- 8.37
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Bayesian Estimation of Spatial Autoregressive Models. https://app.cochat.ai/papers/10.1177/016001769702000107
- The paper
- Lesage, J. P. (1997). Bayesian Estimation of Spatial Autoregressive Models. International Regional Science Review. https://doi.org/10.1177/016001769702000107
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
