arXiv 5 Aug 2021 · Econometrics · publishedJournal of Econometrics (2023) · 3 citations (OpenAlex)
arXiv:2108.02864 · PDF · DOI · OpenAlex · Extracted main text
We consider a high-dimensional model in which variables are observed over time and space. The model consists of a spatio-temporal regression containing a time lag and a spatial lag of the dependent variable. Unlike classical spatial autoregressive models, we do not rely on a predetermined spatial interaction matrix, but infer all spatial interactions from the data. Assuming sparsity, we estimate the spatial and temporal dependence fully data-driven by penalizing a set of Yule-Walker equations. This regularization can be left unstructured, but we also propose customized shrinkage procedures when observations originate from spatial grids (e.g. satellite images). Finite sample error bounds are derived and estimation consistency is established in an asymptotic framework wherein the sample size and the number of spatial units diverge jointly. Exogenous variables can be included as well. A simulation exercise shows strong finite sample performance compared to competing procedures. As an empirical application, we model satellite measured NO2 concentrations in London. Our approach delivers forecast improvements over a competitive benchmark and we discover evidence for strong spatial interactions.
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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 | Gao, Z., Ma, Y., Wang, H., and Yao, Q (2019) Banded spatio-temporal autoregressions | 1.000 | 14 | 5 | 100% |
| 2 | Ma, Y., Guo, S., and Wang, H (2021) Sparse spatio-temporal autoregressions by profiling and bagging | 0.811 | 4 | 2 | 100% |
| 3 | Dou, B., Parrell, M. L., and Yao, Q (2016) Generalized Yule-Walker estimation for spatio-temporal models with unknown diagonal coefficients | 0.737 | 3 | 2 | 100% |
| 4 | Masini, R. P., Medeiros, M. C., and Mendes, E. F (2019) Regularized estimation of high-dimensional vector autoregressions with weakly dependent innovations | 0.737 | 3 | 2 | 100% |
| 5 | Lee, L.-F (2004) Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models | 0.737 | 3 | 2 | 100% |
| 6 | Kock, A. B. and Callot, L (2015) Oracle inequalities for high dimensional vector autoregressions | 0.644 | 2 | 2 | 100% |
| 7 | Lee, L.-F. and Yu, J (2010) Estimation of spatial autoregressive panel data models with fixed effects | 0.644 | 2 | 2 | 100% |
| 8 | Lam, C. and Souza, P. C. L (2019) Estimation and selection of spatial weight matrix in a spatial lag model | 0.585 | 3 | 1 | 100% |
| 9 | Guo, S., Wang, Y., and Yao, Q (2016) High-dimensional and banded vector autoregressions | 0.511 | 2 | 2 | 50% |
| 10 | Simon, N., Friedman, J., Hastie, T., and Tibshirani, R (2013) A sparse-group lasso | 0.511 | 2 | 1 | 100% |
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