arXiv 31 May 2023 · Econometrics · publishedJournal of Business and Economic Statistics (2025)
arXiv:2305.19721 · PDF · DOI · OpenAlex · Extracted main text
With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable to handle the data with a large size. In addition, when establishing the asymptotic properties of the parameter estimators of the SAR model, both weights matrix and regressors are assumed to be nonstochastic in classical spatial econometrics, which is perhaps not realistic in real applications. Motivated by the machine learning literature, this paper proposes quasi-score matching estimation for the SAR model. This new estimation approach is developed based on the likelihood, but significantly reduces the computational complexity of the QMLE. The asymptotic properties of parameter estimators under the random weights matrix and regressors are established, which provides a new theoretical framework for the asymptotic inference of the SAR-type models. The usefulness of the quasi-score matching estimation and its asymptotic inference is illustrated via extensive simulation studies and a case study of an anti-conflict social network experiment for middle school students.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lee, L.-F (2004) Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models | 1.000 | 10 | 3 | 100% |
| 2 | Kelejian, H. H. and Prucha, I. R (1998) A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive distur… | 1.000 | 9 | 5 | 100% |
| 3 | Hyvärinen, A (2005) Estimation of non-normalized statistical models by score matching | 1.000 | 7 | 3 | 100% |
| 4 | Huang, D., Lan, W., Zhang, H. H., and Wang, H (2019) Least squares estimation of spatial autoregressive models for large-scale social networks | 1.000 | 6 | 3 | 100% |
| 5 | Zhu, X., Huang, D., Pan, R., and Wang, H (2020) Multivariate spatial autoregressive model for large scale social networks | 1.000 | 5 | 3 | 100% |
| 6 | Lee, L.-F (2003) Best spatial two-stage least squares estimators for a spatial autoregressive model with autoregressive disturbances | 0.928 | 4 | 4 | 100% |
| 7 | Kelejian, H. H. and Prucha, I. R (2001) On the asymptotic distribution of the $Moran $I$$ test statistic with applications | 0.874 | 6 | 2 | 100% |
| 8 | LeSage, J. P. and Pace, R. K (2009) Introduction to Spatial Econometrics | 0.874 | 5 | 2 | 100% |
| 9 | Gupta, A (2023) Efficient closed-form estimation of large spatial autoregressions | 0.843 | 3 | 3 | 100% |
| 10 | Lee, L.-F (2007) GMM and 2SLS estimation of mixed regressive, spatial autoregressive models | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 28 scored citations.