2014/08/31 by Guanpeng Dong, Richard Harris · 124 citations
Economics, Econometrics and Finance · Mathematics · #Artificial intelligence #Autoregressive model #Bayesian probability #Computer science #Data mining #Data set #Econometric model #Econometrics #Geography #Hierarchical database model #Housing Market and Economics #Markov chain #Markov chain Monte Carlo #Mathematics #Multilevel model #Random effects model #Regional Economics and Spatial Analysis #Spatial analysis #Spatial and Panel Data Analysis #Spatial dependence #Spatial econometrics #Statistics
paper · doi:10.1111/gean.12049
published in Geographical Analysis 47(2), 173-191 (Wiley)
openalex publication_date 2014/08/31 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/21
This article discusses how standard spatial autoregressive models and their estimation can be extended to accommodate geographically hierarchical data structures. Whereas standard spatial econometric models normally operate at a single geographical scale, many geographical data sets are hierarchical in nature—for example, information about houses nested into data about the census tracts in which those houses are found. Here we outline four model specifications by combining different formulations of the spatial weight matrix W and of ways of modeling regional effects. These are (1) groupwise W and fixed regional effects; (2) groupwise W and random regional effects; (3) proximity‐based W and fixed regional effects; and (4) proximity‐based W and random regional effects. We discuss each of these model specifications and their associated estimation methods, giving particular attention to the fourth. We describe this as a hierarchical spatial autoregressive model. We view it as having the most potential to extend spatial econometrics to accommodate geographically hierarchical data structures and as offering the greatest coming together of spatial econometric and multilevel modeling approaches. Subsequently, we provide Bayesian Markov Chain Monte Carlo algorithms for implementing the model. We demonstrate its application using a two‐level land price data set where land parcels nest into districts in Beijing, C hina, finding significant spatial dependence at both the land parcel level and the district level.