HSAR: An R Package for Integrated Spatial Econometric and Multilevel Modelling (original) (raw)
Frontiers in spatial econometrics modelling
Economic Modelling, 2012
The present issue of Economic Modelling features a selection of papers presented at the 3rd World Conference of the Spatial Econometrics Association, held on July 9-10, 2009 at the Faculty of Economics and Business, University of Barcelona. The purpose of this meeting was to provide a forum where economists, econometricians, geographers, and regional scientists could discuss the development of theoretical tools and sound applications of the discipline of spatial econometrics. Henry Overman, from the London School of Economics, and James LeSage, from Texas State University, gave the plenary speeches. The scientific contributions to the conference spanned a broad range of topics: ranging from applied to theoretical econometrics. This variety of papers offered an insight into the scope for innovative empirical research across different areas in the field spatial econometrics, including also spatial statistics and spatial data analysis. The meeting was attended by more than 100 participants from around the world. All of the papers selected for this special issue have gone through the usual process of peer review for Economic Modelling, and we would like to thank all of the referees for their hard work. The variety of the contributions both in terms of the methodological contribution and in terms of the substantive issues tackled provides a good snapshot of the current development of the discipline and of the future paths along which it will develop in future years. Contributions cover both the topic of static spatial econometrics based on synchronic data and that of dynamic modelling based on spatial panel data. This issue contains nine papers. Four of them refer to static spatial econometrics (namely those written by Arbia et al., Seya, et al., Smirnov and Egan and Yokoi and Ando) while the remaining 5 (namely those by Baltagi et al., Bartkowska and Riedl, Cotteleer and Van Kooten, Hall and Guo and Claeys et al.) conversely take an explicit panel data approach. In what follows we briefly summarize the content of the various contributions to this special issue. Most of the existing literature in spatial econometrics uses data at aggregate level. The paper by Arbia, Espa, Giuliani and Mazzitelli take a different approach and present a methodology based on micro data referring to single individual economic agents. Following such an approach they treat each firm as a point in the space and present a methodology to analyse the regularities in their arrangement using point pattern techniques. In particular they propose a nonparametric approach for the analysis of spatial heterogeneity, based on the so-called inhomogeneous K-function. They present an empirical application of the method to analyse the spatial distribution of 2654 high-tech manufacturing plants in Italy, for the year 2001. The authors assume the economic space to be non-homogenous, and take the pattern of inhomogeneity as an instrument to disentangle spatial heterogeneity from spatial dependence. Along similar lines, Baltagi, Blien and Wolf study individual data distributed in space and discuss the possibility of a dynamic 'wage curve'
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.
ERSA Congress, Papers, 2009
Spatial econometrics and also multilevel modelling techniques are increasingly part of the regional scientists" toolbox. Both approaches are used to model spatial autocorrelation in a wide variety of applications. However, it is not always clear on which basis researchers make a choice between spatial econometrics and spatial multilevel modelling. Therefore it is useful to compare both techniques. Spatial econometrics incorporates neighbouring areas into the model design; and thus interprets spatial proximity as defined in Tobler"s first law of geography.
Low rank spatial econometric models
arXiv (Cornell University), 2018
This article presents a restructuring of spatial econometric models in a linear mixed model framework. To that end, it proposes low rank spatial econometric models that are robust to the existence of noise (i.e., measurement error), and can enjoy fast parameter estimation and inference by Type II restricted likelihood maximization (empirical Bayes) techniques. The small sample properties of the proposed low rank spatial econometric models are examined using Monte Carlo simulation experiments; the results of these experiments confirm that direct effects and indirect effects à la LeSage and Pace (2009) can be estimated with a high degree of accuracy. Also, when data are noisy, estimators for coefficients in the proposed models have lower root mean squared errors compared to conventional specifications, despite them being low rank approximations. The proposed approach is implemented in an R package "spmoran."
Spatial Structures and Spatial Spillovers: A GME Approach
ERSA conference papers, 2006
Spatial econometric methods measure spatial interaction and incorporate spatial structure into regression analysis. The specification of a matrix of spatial weights W plays a crucial role in the estimation of spatial models. The elements w ij of this matrix measure the spatial relationships between two geographical locations i and j, and they are specified exogenously to the model. Several alternatives for W have been proposed in the literature, although binary matrices based on contiguity among locations or distance matrices are the most common choices. One shortcoming of using this type of matrices for the spatial models is the impossibility of estimating "heterogeneous" spatial spillovers: the typical objective is the estimation of a parameter that measures the average spatial effect of the set of locations analyzed. Roughly speaking, this is given by "ill-posed" econometric models where the number of (spatial) parameters to estimate is too large. In this paper, we explore the use of generalized maximum entropy econometrics (GME) to estimate spatial structures. This technique is very attractive in situations where one has to deal with estimation of "illposed" or "ill-conditioned" models. We compare by means of Monte Carlo simulations "classical" ML estimators with GME estimators in several situations with different availability of information.
Contemporary developments in the theory and practice of spatial econometrics
Spatial Economic Analysis
The papers in this special issue cover a wide range of areas in the methodology and application of spatial econometrics. The first develops a generalized method of moments (GMM) estimator for the spatial regression model from a second-order approximation to the maximum likelihood (ML). The second develops Bayesian estimation in a stochastic frontier model with network dependence in efficiencies, with application to industry dynamics. The third studies crosscountry convergence under the Lotka-Volterra model and obtains new insights into spatial spillovers. The penultimate paper develops robust specification tests for the social interactions model under both ML and GMM frameworks. The final paper proposes identification and GMM estimation in a high-order spatial autoregressive model with heterogeneity, common factors and spatial error dependence. KEYWORDS spatial econometrics, panel data, social networks, generalized method of moments (GMM), Bayesian methods, Lotka-Volterra model JEL C11, C21, C23, C38, C52 This special issue collects selected papers from the 26th (EC) 2 Conference on the 'Theory and Practice of Spatial Econometrics', organized in December 2015 by the Spatial Economics & Econometrics Centre (SEEC), Heriot-Watt University, Edinburgh, UK. 1 The conference was a great success, with wide participation and high quality of papers. Subsequently, Spatial Economic Analysis, one of the leading field journals in the area, approached the organizers with an invitation to organize a special issue. Unfortunately, the preparation of this special issue also happened in the shadow of the loss of two of the leading researchers in the areas of spatial econometrics and regional science: Cem Ertur (1962-2016) and Raymond J. G. M. Florax (1956-2017). Beyond their important roles as leading researchers in the area, they were also dear friends and colleagues to many of us. Though neither Cem nor Raymond was able to attend the conference in person, they both had significant presence intellectually: in the papers presented at the conference, in the literatures that the presented papers contributed to and, likewise, in the papers in this issue. The papers submitted to this special issue were subjected to the regular review standards and processes of Spatial Economic Analysis. The five selected papers are, individually and collectively,