arXiv 16 Jun 2025 · Econometrics · publishedNetworks and Spatial Economics (2025) · 1 citations (OpenAlex)
arXiv:2506.13682 · PDF · DOI · OpenAlex · Extracted main text
Researchers in urban and regional studies increasingly deal with spatial data that reflects geographic location and spatial relationships. As a framework for dealing with the unique nature of spatial data, various spatial regression models have been introduced. In this article, a novel model-based gradient boosting algorithm for spatial regression models with autoregressive disturbances is proposed. Due to the modular nature, the approach provides an alternative estimation procedure which is feasible even in high-dimensional settings where established quasi-maximum likelihood or generalized method of moments estimators do not yield unique solutions. The approach additionally enables data-driven variable and model selection in low- as well as high-dimensional settings. Since the bias-variance trade-off is also controlled in the algorithm, implicit regularization is imposed which improves prediction accuracy on out-of-sample spatial data. Detailed simulation studies regarding the performance of estimation, prediction and variable selection in low- and high-dimensional settings confirm proper functionality of the proposed methodology. To illustrative the functionality of the model-based gradient boosting algorithm, a case study is presented where the life expectancy in German districts is modeled incorporating a potential spatial dependence structure.
appendix boundary found by appendix_command · 79% of the source is main text. Read the extracted text to check this.
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 | Bühlmann P, Hothorn T (2007) Boosting algorithms: Regularization, prediction and model fitting | 1.000 | 6 | 4 | 100% |
| 2 | Hofner B, Mayr A, Robinzonov N, et al (2014) Model-based boosting in R: A hands-on tutorial using the R package mboost | 0.928 | 4 | 3 | 100% |
| 3 | Hofner B, Boccuto L, Goeker M (2015) Controlling false discoveries in high-dimensional situations: Boosting with stability selection | 0.928 | 4 | 3 | 100% |
| 4 | Anselin L (1988) Spatial econometrics: methods and models, vol 4 | 0.843 | 5 | 4 | 60% |
| 5 | Hothorn T, Buehlmann P, Kneib T, et al (2010) Model-based boosting 2.0 | 0.843 | 3 | 3 | 100% |
| 6 | LeSage J, Pace RK (2009) Introduction to spatial econometrics | 0.843 | 3 | 3 | 100% |
| 7 | Strömer A, Staerk C, Klein N, et al (2022) Deselection of base-learners for statistical boosting—with an application to distributional regression | 0.811 | 4 | 2 | 100% |
| 8 | Bühlmann P (2006) Boosting for high-dimensional linear models | 0.737 | 3 | 3 | 67% |
| 9 | Kelejian HH, Prucha IR (1999) A generalized moments estimator for the autoregressive parameter in a spatial model | 0.737 | 3 | 2 | 100% |
| 10 | Mayr A, Hofner B, Schmid M (2012) The importance of knowing when to stop | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 53 scored citations.