Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe
空间聚类空间自回归模型及其在欧洲农业市场集中度中的应用AI 解读 ↓
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空间聚类空间自回归模型及其在欧洲农业市场集中度中的应用
- 研究问题
- 如何在空间回归中同时刻画邻近地区的依赖与系数异质性，并分析欧洲农业市场集中度的区域分化和局部溢出？
- 方法与思路
- 作者将空间自回归、空间误差等模型扩展为簇内共享系数的结构，通过惩罚最大似然联合估计空间分组与回归系数。
- 材料陈述的贡献
- 摘要称该方法识别出欧洲农业市场的三个区域簇，并发现农业相关解释变量对集中度的作用存在空间及时间异质性。
- 阅读关注点
- 应核对空间权重矩阵、聚类惩罚与簇数的选择方式，以及局部溢出、系数异质性和时间变化如何区分与解释。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明识别条件，区域聚类不能直接证明因果溢出。
原文摘要
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.