Roy Cerqueti, Paolo Maranzano, Raffaele Mattera
arXiv 19 Jul 2024 · Statistics — Methodology · publishedJournal of Agricultural Biological and Environmental Statistics (2025) · 5 citations (OpenAlex)
arXiv:2407.15874 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we present an extension of the spatially-clustered linear regression models, namely, the spatially-clustered spatial autoregression (SCSAR) model, to deal with spatial heterogeneity issues in clustering procedures. In particular, we extend classical spatial econometrics models, such as the spatial autoregressive model, the spatial error model, and the spatially-lagged model, by allowing the regression coefficients to be spatially varying according to a cluster-wise structure. Cluster memberships and regression coefficients are jointly estimated through a penalized maximum likelihood algorithm which encourages neighboring units to belong to the same spatial cluster with shared regression coefficients. Motivated by the increase of observed values of the Gini index for the agricultural production in Europe between 2010 and 2020, the proposed methodology is employed to assess the presence of local spatial spillovers on the market concentration index for the European regions in the last decade. Empirical findings support the hypothesis of fragmentation of the European agricultural market, as the regions can be well represented by a clustering structure partitioning the continent into three-groups, roughly approximated by a division among Western, North Central and Southeastern regions. Also, we detect heterogeneous local effects induced by the selected explanatory variables on the regional market concentration. In particular, we find that variables associated with social, territorial and economic relevance of the agricultural sector seem to act differently throughout the spatial dimension, across the clusters and with respect to the pooled model, and temporal dimension.
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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 | Shonosuke Sugasawa and Daisuke Murakami (2021) Spatially clustered regression | 0.961 | 9 | 6 | 89% |
| 2 | J Paul Elhorst (2014) Spatial Econometrics: From Cross-Sectional Data to Spatial Panels | 0.843 | 3 | 3 | 100% |
| 3 | Richard J Hathaway and James C Bezdek (1993) Switching regression models and fuzzy clustering | 0.737 | 3 | 2 | 100% |
| 4 | Helmuth Späth (1979) Algorithmus 39. klassenweise lineare regression | 0.737 | 3 | 2 | 100% |
| 5 | Shonosuke Sugasawa (2021) Grouped heterogeneous mixture modeling for clustered data | 0.737 | 3 | 2 | 100% |
| 6 | Sarah K. Lowder, Jakob Skoet, and Terri Raney (2016) The number, size, and distribution of farms, smallholder farms, and family farms worldwide | 0.644 | 4 | 1 | 100% |
| 7 | Sarah K. Lowder, Marco V. Sánchez, and Raffaele Bertini (2021) Which farms feed the world and has farmland become more concentrated? | 0.644 | 4 | 1 | 100% |
| 8 | Bertan Ari and H Altay Güvenir (2002) Clustered linear regression | 0.644 | 2 | 2 | 100% |
| 9 | Renato Coppi, Pierpaolo D’Urso, Paolo Giordani, and Adriana Santoro (2006) Least squares estimation of a linear regression model with lr fuzzy response | 0.644 | 2 | 2 | 100% |
| 10 | Renfrey Burnard Potts (1952) Some generalized order-disorder transformations | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 46 scored citations.