Advances in spatial econometrics and geostatistics: methods, theory, and applications

New spatial econometric techniques and applications in regional science

With its roots in geography and regional science spatial analysis has experienced remarkable growth in recent years in terms of theory, methods, and applications. The series of books, that in the past decade have collected research in spatial analysis and econometrics, provide both documented evidence and a powerful platform to further this upwards trend. Among the collections that have done so stand those compiled by Anselin and Florax (New Directions in Spatial Econometrics, 1994), Fischer and Getis (Recent Developments in Spatial Analysis, 1997), and Anselin, Florax and Rey (Advances in Spatial Econometrics, 2004). In the spirit of this series of volumes, the present book aims at promoting the development and use of methods for the analysis of spatial data and processes. Traditionally, the core audience for the spatial analysis literature has been found in the Quantitative Geography and Regional Science communities, but also increasingly within the allied disciplines of Spatial and Regional Economics, Urban and Regional Planning and Development, Civil Engineering, Real Estate Studies, and Epidemiology, among others. Previous edited volumes, in particular the two spatial econometrics collections cited above, tended to emphasize, in addition to theoretical and methodological developments, economics and regional economics applications. In this book, we have made an attempt to capture a broader cross-section of themes, to include fields where spatial analysis has represented in recent years a boon for applications, which have in turn encouraged further technical developments. Besides the disciplines represented in previous collections of papers, up-and-coming areas that are seen to be making more extensive use of spatial analytical tools include transportation and land use analysis, political and economic geography, and the analysis of population and health issues. In order to provide a faithful picture of the current state of spatial analysis it is also our wish to present recent theoretical and methodological developments. Together, this collection of theoretical and methodological papers, and thematic applications, will project, we hope, the image of a thriving and dynamic field, with wide-ranging intellectually stimulating challenges, and rich opportunities for applied research that promises to promote and advance data analysis in a variety of fields.

Since Paelinck coined the term ‘spatial econometrics’ in the early 1970s to refer to a set of methods that deal with the explicit treatment of space in multiregional models, the field has come a long way. The early results established in regional economics by Blommestein, Hordijk, Klaassen, Nijkamp, Paelinck and others [e.g., Hordijk and Nijkamp (1977), Hordijk (1979), Paelinck and Klaassen (1979), Blommestein (1983)], as well as the path breaking work of Cliff and Ord in geography [Cliff and Ord (1973, 1981), Ord (1975)] have grown into a broad set of models, tests and estimation techniques that incorporate space more effectively in econometric modeling [for recent overviews, see Anselin (1988, 1992a), Haining (1990), Cressie (1991)]. In spite of these important methodological developments, it would be an overstatement to suggest that spatial econometrics has become accepted practice in current empirical research in regional science and regional economics. On the positive side, the sad State of affairs reflected in the literature surveys of Anselin and Griffith (1988) and Anselin and Hudak (1992) seems to have taken a turn for the better, since there is evidence of an increased awareness of the importance of space in recent empirical work in ‘mainstream’ economics. For example, this is indicated by the use of spatial models in the study of fiscal spill-overs in Case et al. (1993), the analysis of the productivity effects of public sector capital in Holtz-Eakin (1994), and the assessment of land price volatility in Benirschka and Binkley (1994), among others.

This article appraises recent advances in the spatial econometric literature. It serves as the introduction to a collection of new papers on spatial econometric data analysis brought together in this special issue, dealing specifically with new extensions to the spatial econometric modeling perspective. Although the initial development of the field of spatial econometrics has been rather slow, the Dixit-Stiglitz revolution and the emergence of the New Economy Geography have been instrumental in uplifting the significance and the use of spatial data analysis techniques. Concurrent developments in other social sciences parallel this situation in economics. The upsurge in spatial econometrics is, among other things, driven by the recognition that traditional spatial econometric models are insufficient to capture modern theoretical developments. Therefore, this issue brings together a collection of articles on space-time and discrete choice modeling, spatial nonstationarity, and the methodology and empirics of regional economic growth models.

This study introduces a new method called Spatial Econometric Computable General Equilibrium (SECGE), which integrates both spatial econometrics with computable general equilibrium modeling to improve the effectiveness of impact analysis on transportation infrastructure. Elasticities of factor substitution for the Constant Elasticity Substitution (CES) production function are estimated in a spatial econometric model with consideration of spatial dependence. CGE simulations adopting different substitution elasticities show a difference between spatial econometric estimation and traditional OLS estimation. Although the differences are relatively small in this aggregate case study, implications for more sensitive disaggregated regional models are clear.

Spatial econometrics came into being in the 1970s. The reputation of spatial econometrics has become more and more popular after that. It attracted many scholars at home and abroad. After years of research, space metrology has gradually matured. Compared with traditional econometrics, spatial econometrics uses spatial correlation to combine some seemingly independent individuals to build a spatial model to help researchers carry out detailed data analysis through a certain relationship. In this paper, it summarizes the theory of spatial econometrics and the differences between spatial econometrics and traditional econometrics. Then, an economic model is established by using spatial econometrics to study the regional production capacity of China based on the static and dynamic spatial panel.

This study introduces a new method called Spatial Econometric Computable General Equilibrium (SECGE) model, which integrates both spatial econometrics with computable general equilibrium modeling to improve the effectiveness of impact analysis on transportation infrastructure. Elasticities of factor substitution for the Constant Elasticity Substitution (CES) production function is estimated in spatial econometric models with consideration of spatial dependence. CGE simulations adopting different elasticities of factor substitution show a difference between spatial econometric estimation and traditional OLS estimation. Although the differences are relatively small in this aggregate case study, implications for more sensitive disaggregated regional models are clear.

How (Not) to Lie with Spatial Statistics

ABSTRACTWe provide a comprehensive overview of the strengths and weaknesses of different spatial econometric model specifications in terms of spillover effects. Based on this overview, we advocate taking the SLX model as point of departure in case a well‐founded theory indicating which model is most appropriate is lacking. In contrast to other spatial econometric models, the SLX model also allows for the spatial weights matrix W to be parameterized and the application of standard econometric techniques to test for endogenous explanatory variables. This starkly contrasts commonly used spatial econometric specification strategies and is a complement to the critique of spatial econometrics raised in a special theme issue of the Journal of Regional Science (Volume 52, Issue 2). To illustrate the pitfalls of the standard spatial econometrics approach and the benefits of our proposed alternative approach in an empirical setting, the Baltagi and Li (2004) cigarette demand model is estimated.

In the introduction to New Directions in Spatial Econometrics (Anselin and Florax, 1995b), the precursor to the current volume, we set out by arguing that “it would be an overstatement to suggest that spatial econometrics has become accepted practice in current empirical research in regional science and regional economics.” However, we also pointed out that “there is evidence of an increased awareness of the importance of space in recent empirical work in ‘mainstream’ economics” (Anselin and Florax, 1995a, p. 3). In the few years since New Directions appeared, the latter observation has been confirmed by a tremendous growth in the number of publications in which spatial econometric techniques are applied, not only within regional science and economic geography, but also increasingly in the leading journals of economics, sociology and political science. This has not gone unnoticed, and the wealth of new publications has resulted in a separate classification in the Journal of Economic Literature devoted solely to cross-sectional and spatial models.1 Parallelling the growth in applications, several new methods have been introduced as well, yielding a spatial econometric toolbox that is becoming ever more sophisticated.

Spatial econometrics is a branch discipline of econometrics, which mainly studies the traditional econometric models with spatial effect. Based on the development of spatial econometrics context, this paper gives a brief survey about the main spatial econometric model form, estimation and testing methods and the main applications of spatial econometric. And from the cross-sectional data point of view, this paper discusses a set of several classic spatial econometric models and maximum likelihood estimation method in detail.

This study employs spatial econometric methods to examine the effects of economic complexity, renewable and fossil energy consumption, and globalization on the load capacity factor of 25 European Union countries between 1995 and 2021. Addressing a critical gap in existing literature, this research is the first to examine the spillover effects of the load capacity factor and the influence of neighboring countries’ economic complexity on the local country’s environmental quality. The use of spatial econometric techniques is crucial in this context, as it allows for a deeper understanding of how independent variables in neighboring countries may affect the dependent variables in the local country. This study is novel in testing the Load Capacity Curve (LCC) hypothesis using the Economic Complexity Index (ECI) as a multifaceted indicator of economic development. The study also provides a unique analysis of the LCC hypothesis in a context that has not been previously explored, namely the European Union, and is the first to investigate the transmission of environmental shocks among neighboring countries. The results reveal a positive relationship between ECI and environmental quality but do not support the LCC hypothesis. The spatial econometric estimates reveal that the load capacity factor has substantial spillover effects among EU countries. The direct and indirect coefficients indicate that globalization and fossil energy reduce environmental quality in neighboring countries, whereas renewable energy improves it. The spatial estimates indicate that environmental shocks are transmitted to neighboring countries. Based on these findings, several policy recommendations are discussed.

Spatial econometrics has studied and analyzed the horizontal interactions that take place between different geographic locations. The proximity between two locations makes them behave more similarly than those locations that are further away. The development of this literature has been possible, in part, due to the increase in disaggregated data at the geographical level. This disaggregation also allows us to have data at different geographic scales (i.e., provinces, regions, and countries), ending in nested data sets. This nested nature of the data allows and generates the need to take into account the possible vertical spillovers that occur when a higher scale can influence the lower scales, for example, countries that influence their regions. In recent years, some authors have proposed different models that allow the inclusion of both types of interactions, vertical and horizontal. However, the literature and the empirical applications are still scarce. For this reason, this thesis tries to empirically analyze these models and to develop new models that allow progress in the inclusion of vertical spillovers in the field of spatial econometrics. Through applications in the sensitivity of the regions to the economic cycle, self-employment, cigarette consumption and the productivity of the European countries and regions, different proposed models are analyzed, such the dynamic spatial econometrics model with common factors and hierarchical spatial econometrics models. Chapter 2 analyze which regions are more sensitive to aggregate fluctuations, finding a pattern for Spain where the most sensitive regions are on the Mediterranean coast. Chapter 3 analyzes the spatial dynamics of self-employment in the United States, finding a relationship between high self-employment clusters and sensitivity to the national cycle. In chapter 4 and 5, cigarette consumption in the Spanish provinces is analyzed and the price is modelled as a common national factor, finding heterogeneity in the behaviour of the provinces. Finally, Chapter 6 develops an HSD model of spatial econometrics in a hierarchical context and is applied to analyze the production of European regions and the influence of countries on them.

Nowadays the spatial econometrics is became widely used in transportation sciences. In order to know which method can be used or how they should be used, the review articles give answers. In this recent paper the goal is to collect all of the methods in one article which can be used in further researches. The improvement in this article is that beside the spatial econometric methods, other necessary techniques are also introduced. With this fact a whole analysis can be applied.

Spatial econometric analyses abound in spatial sciences such as regional and urban economics, geography, and planning. Although the origins of spatial econometrics can be largely traced back to the disciplines of economics and geography, the field of application has by now stretched out as far as political science, sociology, ecology, and (transportation) engineering — to name a few. A recent search in the Web of Science’s core collection of the Social Sciences Citation Index shows 634 publications under the topic spatial econometrics.