HSAR: An R Package for Integrated Spatial Econometric and Multilevel Modelling (original) (raw)
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Empirical Models of Spatial Inter‐Dependence
Oxford Handbooks Online, 2008
This article discusses the role of ‘spatial interdependence’ between units of analysis by using a symmetric weighting matrix for the units of observation whose elements reflect the relative connectivity between unitiand unitj. It starts by addressing spatial interdependence in political science. There are two workhorse regression models in empirical spatial analysis: spatial lag and spatial error models. The article then addresses OLS estimation and specification testing under the null hypothesis of no spatial dependence. It turns to the topic of assessing spatial lag models, and a discussion of spatial error models. Moreover, it reports the calculation of spatial multipliers. Furthermore, it presents several newer applications of spatial techniques in empirical political science research: SAR models with multiple lags, SAR models for binary dependent variables, and spatio-temporal autoregressive (STAR) models for panel data.
PloS one, 2015
This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership structure-for example, census zones into regions) but the actual locations of the units and the distances between them are not directly considered: what matters is the groupings but not how close together any two units are within those groupings. As a consequence, spatial interaction effects are neither modelled nor measured, confounding group effects (understood as some sort of contextual effect that acts 'top down' upon members of a group) with proximity effects (some sort of joint dependency that emerges between neighbours). To deal with this, we incorporate spatial simultaneous autoregressive processes into both the outcome variable and the higher level residuals. To assess the ...
Applied Spatial Econometrics: Raising the Bar
Spatial Economic Analysis, 2010
This paper places the key issues and implications of the new 'introductory' book on spatial econometrics by James LeSage & Kelley Pace (2009) in a broader perspective: the argument in favour of the spatial Durbin model, the use of indirect effects as a more valid basis for testing whether spatial spillovers are significant, the use of Bayesian posterior model probabilities to determine which spatial weights matrix best describes the data, and the book's contribution to the literature on spatiotemporal models. The main conclusion is that the state of the art of applied spatial econometrics has taken a step change with the publication of this book.
2018
The applications of standard regression analysis on spatial data are not appropriate because of the characteristics of the spatial data. Spatial data has two characteristics are spatial dependence and spatial heterogeneity. Modeling spatial data using standard linear regression model leads to bias, inconsistency and inefficient results. Several models have been developed to accommodate the characteristics of the spatial data. However, the models generally developed to solve only one problem of the spatial data (e.g., spatial dependence or spatial heterogeneity). Four kinds of spatial econometrics models usually used to accommodate spatial dependence are spatial autoregressive (SAR), spatial lagged exogenous variables (SLX), spatial error model (SEM), and spatial Durbin model (SDM). To accommodate the spatial heterogeneity, geographically weighted regression (GWR) or varying coefficient model (VCM) is usually used. Our research proposed to develop a new model to accommodate two chara...
2019
This paper presents a conceptual and empirical framework to deal with complex spatial dependence patterns. We introduce the concept of multi-dimensional spatial weight matrix to capture more complex, asymmetric spatial interactions, including spillovers that are not necessarily conditioned by geographic distance. Most spatial studies rely merely on geographic proximity as a result of the parameter interpretation challenges associated with complex spatial structures. This paper fills this gap by suggesting an appropriate instrumental variables (IV) estimation procedure for spatial models incorporating multi-dimensional spatial weight matrices. We provide an empirical application of the multi-dimensional spatial auto-regressive (MSAR) model. We use a three-dimensional spatial weight matrix including geographical distance as well as socioeconomic factors such as economic size and human capital endowment. We find that compared to the traditional Spatial Durbin Model (SDM), the MSAR mode...
A structural equation approach to spatial dependence models
In this paper we propose a Structural Equations Model (SEM) approach to spatial dependence models. Latent variables are used to represent spatial spill-over effects in the structural model of which the observed spatially lagged variables are indicators. This approach allows for more information and modeling flexibility than the representation of spatial spill-over effects in terms of Wy or Wx. Furthermore, we propose a Full Information Maximum Likelihood (FIML) estimator as an alternative to the estimators commonly used, notably the iterative and two-stage estimators for the error and lag model, respectively. We also show that the estimation procedures included in the software packages Mx and LISREL 8 to estimate SEMs can be applied in a straightforward way to estimate spatial dependence models in a standard fashion.
The theory and practice of spatial econometrics
1999
This text provides an introduction to spatial econometric theory along with numerous applied illustrations of the models and methods described. The applications utilize a set of MATLAB functions that implement a host of spatial econometric estimation methods. The intended audience is faculty, students and practitioners involved in modeling spatial data sets. The MATLAB functions described in this book have been used in my own research as well as teaching both undergraduate and graduate econometrics courses. They are available on the Internet at http://www.econ.utoledo.edu along with the data sets and examples from the text.