Statistical and Methodological Advances in Spatial Economics: A Comprehensive Review of Models, Empirical Strategies, and Policy Evaluation

This study brings together current advances in the statistical and methodological foundations of spatial economics, focusing on the use of quantitative models and empirical approaches to investigate the distribution of economic activity over geographic space. We combine classical principles with modern approaches that emphasize causal identification, structural estimation, and the use of statistical and computational tools such as spatial econometrics, machine learning, and big data analytics. The study focuses on methodological challenges in spatial data analysis, such as spatial autocorrelation, high dimensionality, and the use of Geographic Information Systems (GIS), while also discussing advances in the design and estimation of quantitative spatial models. The focus is on contemporary empirical applications that use natural experiments, quasi-experimental approaches, and advanced econometric tools to examine the effects of agglomeration, market access, and infrastructure policy. Despite significant advances, significant challenges remain in resilient model identification, dynamic analysis, and the integration of statistical approaches with new types of geographic data. This page focuses on statistical methodologies and serves as a resource for economists and the broader statistics community interested in spatial modeling, causal inference, and policy evaluation.