arXiv 17 Jan 2023 · Econometrics
arXiv:2301.06658 · PDF · DOI · OpenAlex · Extracted main text
The spatial dependence in mean has been well studied by plenty of models in a large strand of literature, however, the investigation of spatial dependence in variance is lagging significantly behind. The existing models for the spatial dependence in variance are scarce, with neither probabilistic structure nor statistical inference procedure being explored. To circumvent this deficiency, this paper proposes a new generalized logarithmic spatial heteroscedasticity model with exogenous variables (denoted by the log-SHE model) to study the spatial dependence in variance. For the log-SHE model, its spatial near-epoch dependence (NED) property is investigated, and a systematic statistical inference procedure is provided, including the maximum likelihood and generalized method of moments estimators, the Wald, Lagrange multiplier and likelihood-ratio-type D tests for model parameter constraints, and the overidentification test for the model diagnostic checking. Using the tool of spatial NED, the asymptotics of all proposed estimators and tests are established under regular conditions. The usefulness of the proposed methodology is illustrated by simulation results and a real data example on the house selling price.
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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 | Sato, T. and Matsuda, Y (2017) Spatial autoregressive conditional heteroskedasticity models | 0.874 | 5 | 2 | 100% |
| 2 | Jenish, N. and Prucha, I. R (2012) On spatial processes and asymptotic inference under near-epoch dependence | 0.855 | 8 | 4 | 62% |
| 3 | Lee, L. F (2007) GMM and 2SLS estimation of mixed regressive, spatial autoregressive models | 0.843 | 4 | 3 | 75% |
| 4 | Xu, X. and Lee, L. F (2015) A spatial autoregressive model with a nonlinear transformation of the dependent variable | 0.843 | 4 | 3 | 75% |
| 5 | LeSage, J. P. and Pace, R. K (2007) A matrix exponential spatial specification | 0.843 | 3 | 3 | 100% |
| 6 | Jenish, N. and Prucha, I. R (2009) Central limit theorems and uniform laws of large numbers for arrays of random fields | 0.769 | 11 | 3 | 45% |
| 7 | Sato, T. and Matsuda, Y (2021) Spatial extension of generalizeized autoregressive conditional heteroskedasticity models | 0.737 | 3 | 2 | 100% |
| 8 | Xu, X. and Lee, L. F (2015) Maximum likelihood estimation of a spatial autoregressive Tobit model | 0.737 | 3 | 2 | 100% |
| 9 | Cliff, A. D. and Ord, J. K (1981) Spatial Processes: Models $&$ Applications | 0.644 | 2 | 2 | 100% |
| 10 | Fingleton, B (2008) A generalizeized method of moments estimator for a spatial model with moving average errors, with application to real estate pri… | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Spatial and Spatiotemporal Volatility Models: A Review | 0.737 | 3 | 2 |