Automatic selection of a spatial weight matrix in spatial econometrics: Application to a spatial hedonic approach

Value of urban views in a bay city: Hedonic analysis with the spatial multilevel additive regression (SMAR) model

“…Similar tendencies were also found for the variable "Green", that is, the estimate is negative in the MR model, but it is positive in the spatial models. These results reiterate the importance of considering spa- tial dependences, which has empirically been suggested elsewhere (e.g., Brasington & Hite, 2005;Seya et al, 2013).…”

Car Travel Demand: Spillovers and Asymmetric Price Effects in a Spatial Setting

“…Feeding into this framework, future innovations could capture transport network effects in more advanced spatial matrix specifications. There are rapid developments regarding the spatial matrix construction in other fields, where weight matrices capture a range of complicated data linkage structures in social network settings (Bramoullé et al, 2009), or by space-time decay (Thanos et al, 2015;Dubé et al, 2017), or by trans-dimensional simulated annealing algorithms for efficient matrix selection (Seya et al, 2013). These tools are ideal for condensing and analyzing large amounts of data, made possible by the recent advances in computational power.…”

Land Bank 2.0: An Empirical Evaluation

“…A home's own price is excluded from the calculation by setting all values in the matrix diagonal to zero. The optimal specification of spatial weight matrices is an active area of research (Bhattacharjee and Jensen-Butler, 2013;Seya, Yamagata, and Tsutsumi, 2013;Gerkman and Ahlgren, 2014;Qu and Lee, 2015). Stakhovych and Bijmolt (2009) found that in simulated data, where the true data-generating process is known, selecting W using the Akaike information criterion (AIC) could reliably lead to models that accurately estimated regression coefficients.…”