Methodological Developments in Spatial Econometrics and Statistics

The November 2002 North American Meetings of the Regional Science Association International in San Juan, Puerto Rico included five sessions with roughly 20 papers devoted to spatial statistics and econometrics. These paper presentations reflected recent areas of interest by those engaged in methodological as well as applied spatial statistical research. This special issue includes five papers that are representative of current methodological developments as well as innovative application and extension of existing methods. Many traditional spatial statistical estimation methods rely on a weight matrix to model connectivity relations between the units of observation. Not surprisingly, much research centers on determining an appropriate specification of this weight matrix. Two papers in this special issue represent work in this arena, one by Arthur Getis and Jared Aldstadt entitled “Using Local Statistics in the Specification of the Spatial Weights Matrix,” and another by Donald Lacombe, “Does Econometric Methodology Matter? An Analysis of Public Policy Using Spatial Econometric Techniques.” The Getis and Aldstadt paper proposes partitioning the spatial structure into two parts, one that reflects pairwise spatial relations among the observations and the other that models the individual contribution of unconnected observations. In contrast to the classical approaches in spatial statistics and econometrics, which specify global spatial relations on hypothetical grounds or by “practical convenience” as either distance-based functions or neighborhood relations, the Getis and Aldstadt approach estimates the spatial relations of each observation from the data. They apply a local statistical concept, the Gi statistic, to determine the range beyond which no more spatial dependence for each observation can be expected. A series of simulation experiments compares the proposed weight matrix against other specifications of weight.