Sylvain Barde, Rowan Cherodian, Guy Tchuente
arXiv 4 Oct 2023 · Econometrics · publishedEconometrics Journal (2025) · 1 citations (OpenAlex)
arXiv:2310.02773 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a Lasso-based estimator which uses information embedded in the Moran statistic to develop a selection procedure called Moran's I Lasso (Mi-Lasso) to solve the Eigenvector Spatial Filtering (ESF) eigenvector selection problem. ESF uses a subset of eigenvectors from a spatial weights matrix to efficiently account for any omitted cross-sectional correlation terms in a classical linear regression framework, thus does not require the researcher to explicitly specify the spatial part of the underlying structural model. We derive performance bounds and show the necessary conditions for consistent eigenvector selection. The key advantages of the proposed estimator are that it is intuitive, theoretically grounded, and substantially faster than Lasso based on cross-validation or any proposed forward stepwise procedure. Our main simulation results show the proposed selection procedure performs well in finite samples. Compared to existing selection procedures, we find Mi-Lasso has one of the smallest biases and mean squared errors across a range of sample sizes and levels of spatial correlation. An application on house prices further demonstrates Mi-Lasso performs well compared to existing procedures.
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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 | Seya, Murakami, Tsutsumi \ Yamagata (2015) `Application of lasso to the eigenvector selection problem in eigenvector-based spatial filtering', Geographical Analysis 47(3),… | 1.000 | 9 | 4 | 100% |
| 2 | Tiefelsdorf \ Griffith (2007) `Semiparametric filtering of spatial autocorrelation: the eigenvector approach', Environment and Planning A 39(5), 1193–1221 | 1.000 | 9 | 4 | 100% |
| 3 | Griffith (2003) Spatial autocorrelation and spatial filtering: gaining understanding through theory and scientific visualization, Springer Scien… | 0.874 | 8 | 2 | 100% |
| 4 | Griffith (2000) `A linear regression solution to the spatial autocorrelation problem', Journal of Geographical Systems 2(2), 141–156 | 0.874 | 5 | 2 | 100% |
| 5 | Chetverikov, Liao \ Chernozhukov (2020) `On cross-validated lasso in high dimensions', Annals of Statistics 40 | 0.644 | 2 | 2 | 100% |
| 6 | Kelejian \ Piras (2017) Spatial econometrics, Academic Press | 0.644 | 2 | 2 | 100% |
| 7 | Moran (1950) `Notes on Continuous Stochastic Phenomena', Biometrika 37(1-2), 17–23 | 0.644 | 2 | 2 | 100% |
| 8 | Chun, Griffith, Lee \ Sinha (2016) `Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters', Journal of Geographical Sy… | 0.585 | 3 | 1 | 100% |
| 9 | LeSage \ Pace (2014) `The biggest myth in spatial econometrics', Econometrics 2(4), 217–249 | 0.585 | 3 | 1 | 100% |
| 10 | Zhao \ Yu (2006) `On model selection consistency of lasso', Journal of Machine learning research 7(Nov), 2541–2563 | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 54 scored citations.