The DOI matches its Crossref record.
Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models
Abstract
This paper investigates asymptotic properties of the maximum likelihood estimator and the quasi-maximum likelihood estimator for the spatial autoregressive model. The rates of convergence of those estimators may depend on some general features of the spatial weights matrix of the model. It is important to make the distinction with different spatial scenarios. Under the scenario that each unit will be influenced by only a few neighboring units, the estimators may have -rate of convergence and be asymptotically normal. When each unit can be influenced by many neighbors, irregularity of the information matrix may occur and various components of the estimators may have different rates of convergence.
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Cited by
Inference with dependent data using cluster covariance estimators
Bester, C. A., Conley, T. G., & Hansen, C. B. (2011) · Journal of Econometrics · doi:10.1016/j.jeconom.2011.01.007 · Cited by 258
Technology spillover and TFP growth: a spatial Durbin model
Tientao, A., Legros, D., & Pichery, M. C. (2016) · International Economics · doi:10.1016/j.inteco.2015.04.004 · Cited by 81
Metadata
- Authors
- Lee, L.-F.
- Venue
- Econometrica
- Publisher
- The Econometric Society
- Published
- 2004-11
- Volume
- 72
- Issue
- 6
- Pages
- 1899-1925
- ISSN
- 0012-9682 (ISSN-L)
- 1468-0262
- OpenAlex
- W2071673199
- Cited by
- 1,034
- References
- 0
- FWCI
- 31.61
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
