THE SLX MODEL

We 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.     

WHERE IS THE ECONOMICS IN SPATIAL ECONOMETRICS?*

ABSTRACT Spatial econometrics has been criticized by some economists because some model specifications have been driven by data‐analytic considerations rather than having a firm foundation in economic theory. In particular, this applies to the so‐called W matrix, which is integral to the structure of endogenous and exogenous spatial lags, and to spatial error processes, and which are almost the sine qua non of spatial econometrics. Moreover, it has been suggested that the significance of a spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper, we review the theoretical and empirical rationale for network dependence and spatial externalities as embodied in spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes.     

Rook or Queen, But How About Some Other Royal Court Suite Members?

The contribution to spatial econometrics of the Cliff–Ord publication is fully recognized, but it is also shown that it should be complemented by some important spatial econometrics features.     

现代服务业集聚形成机理空间计量分析

在纳入空间效应前提下,构建现代服务业集聚形成机理空间面板计量模型,对我国28个省域相关数据实证研究表明:我国现代服务业集聚在省域之间有较强的空间依赖性和正的空间溢出效应。技术差异在时间维度上对现代服务业集聚促进作用显著,在空间维度上并不显著;交易费用与现代服务业集聚有显著的负相关性;知识溢出、规模经济、政府行为对现代服务业集聚促进作用显著。     

A History of the Concept of Spatial Autocorrelation: A Geographer's Perspective

Spatial autocorrelation is a concept that helps to define the field of spatial analysis. It is central to studies using spatial statistics and spatial econometrics. In this paper, we trace the early development of the concept and explain the academic links that brought the concept to the fore in the late 1960s. In geography, the importance of the work of Michael F. Dacey, Andrew D. Cliff, and J. Keith Ord is emphasized. Later, with the publication of a volume on spatial econometrics by Luc Anselin, spatial research and the use of the concept of spatial autocorrelation received a considerable boost. These developments are outlined together with comments about recent and possible future trends in spatial autocorrelation-based research.     

Stochastic Efficiency of Bayesian Markov Chain Monte Carlo in Spatial Econometric Models: An Empirical Comparison of Exact Sampling Methods

Spatial econometric specifications pose unique computational challenges to Bayesian analysis, making it difficult to estimate models efficiently. In the literature, the main focus has been on extending Bayesian analysis to increasingly complex spatial models. The stochastic efficiency of commonly used Markov Chain Monte Carlo (MCMC) samplers has received less attention by comparison. Specifically, Bayesian methods to analyze effective sample size and samplers that provide large effective size have not been thoroughly considered in the literature. Thus, we compare three MCMC techniques: the familiar Metropolis‐within‐Gibbs sampling, Slice‐within‐Gibbs sampling, and Hamiltonian Monte Carlo. The latter two methods, while common in other domains, are not as widely encountered in Bayesian spatial econometrics. We assess these methods across four different scenarios in which we estimate the spatial autoregressive parameter in a mixed regressive, spatial autoregressive specification (or, spatial lag model). We find that off‐the‐shelf implementations of the newer high‐yield simulation techniques require significant adaptation to be viable. We further find that the effective sizes are often significantly smaller than nominal sizes. In addition, we find that stopping simulation early may understate posterior credible interval widths when effective sample size is small. More broadly, we suggest that sample information and stopping rules deserve more attention in both applied and basic Bayesian spatial econometric research.     

INTRODUCTION: WHITHER SPATIAL ECONOMETRICS?

ABSTRACT Spatial econometrics has become a mainstay for regional scientists aiming to estimate geographic spillovers in regional outcomes. Yet, many remain skeptical, especially urban economists who prefer natural experimental approaches. Their concerns revolve around identification and a general lack of a theoretical foundation in the estimation of spatial econometric models. This theme issue includes three papers from leading regional scientists to appraise the status of spatial econometrics. The outcome is sweeping proposals from (1) abandoning standard spatial econometrics because it cannot identify causality, (2) using nonparametric approaches, and (3) implementing more nuanced changes revolving around better theoretical and empirical modeling.     

Spatial Autoregressive Models for Geographically Hierarchical Data Structures

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, China, finding significant spatial dependence at both the land parcel level and the district level.     

The Spatiotemporal Evolution of Sydney's Tram Network Using Network Econometrics

This paper examines the evolution of Sydney trams using network econometrics approaches. Network econometrics extends spatial econometrics by developing weight matrices based on the physical structure of the network, allowing for competing and complementary elements to have distinct effects. This research establishes a digitized database of Sydney historical tramway network, providing a complete set of geo-referenced data of the opening and closing year and frequencies by time of day for every line. An autoregressive distributed lag model is specified and reveals that the combination of correlation strength and magnitude of lagged flow change on correlated links is a significant predictor of future tram service. The results indicate that complementary and competitive links play distinct roles in shaping the network structure. A link is more likely to undergo the same structural change highly complementary (upstream or downstream) links underwent previously, where the influence is measured by a combination of correlation strength and link importance, reflected by historical service levels.     

BAYSIAN INFERENCE FOR ORDERED RESPONSE DATA WITH A DYNAMIC SPATIAL‐ORDERED PROBIT MODEL

ABSTRACT Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial‐ordered probit (DSOP) model in order to capture patterns of spatial and temporal autocorrelation in ordered categorical response data. This model is estimated in a Bayesian framework using Gibbs sampling and data augmentation, in order to generate all autocorrelated latent variables. It incorporates spatial effects in an ordered probit model by allowing for interregional spatial interactions and heteroskedasticity, along with random effects across regions or any clusters of observational units. The model assumes an autoregressive, AR(1), process across latent response values, thereby recognizing time‐series dynamics in panel data sets. The model code and estimation approach is tested on simulated data sets, in order to reproduce known parameter values and provide insights into estimation performance, yielding much more accurate estimates than standard, nonspatial techniques. The proposed and tested DSOP model is felt to be a significant contribution to the field of spatial econometrics, where binary applications (for discrete response data) have been seen as the cutting edge. The Bayesian framework and Gibbs sampling techniques used here permit such complexity, in world of two‐dimensional autocorrelation.     

Disability activism and the politics of scale

In this paper, we examine the role of spatial scale in mediating and shaping political struggles between disabled people and the state. Specifically, we draw on recent theoretical developments concerning the social construction of spatial scale to interpret two case studies of disability activism within Canada and Ireland. In particular, we provide an analysis of how successful the disability movement in each locale has been at 'jumping scale' and enacting change, as well as examining what the consequences of such scaling‐up have been for the movement itself. We demonstrate that the political structures operating in each country markedly affect the scaled nature of disability issues and the effectiveness of political mobilization at different scales .     

中国城市人口集聚分布——基于空间效应的研究

城市人口集聚分布以及城市带的形成是一个空间现象,空间力量对其形成和演化具有重要影响。本文应用空间计量经济学与趋势面分析相结合方法,以617个县级以上城市1998年和2011年的城镇人口和经纬度坐标数据来研究中国城市人口空间集聚分布与趋势。研究发现,中国城市人口分布与其周围相邻城市的人口分布关系密切,并且其6阶空间自相关效应是最强的;同时地理位置对中国城市人口体系的空间分布和纵向形态形成具有显著影响。在空间自相关和空间位置相关的共同作用下,城市人口规模聚集区已在东部沿海地区形成。     

A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity

There is an increasing awareness of the potentials of nonlinear modeling in regional science. This can be explained partly by the recognition of the limitations of conventional equilibrium models in complex situations, and also by the easy availability and accessibility of sophisticated computational techniques. Among the class of nonlinear models, dynamic variants based on, for example, chaos theory stand out as an interesting approach. However, the operational significance of such approaches is still rather limited and a rigorous statistical-econometric treatment of nonlinear dynamic modeling experiments is lacking. Against this background this paper is concerned with a methodological and empirical analysis of a general misspecification test for spatial regression models that is expected to have power against nonlinearity, spatial dependence, and heteroskedasticity. The paper seeks to break new research ground by linking the classical diagnostic tools developed in spatial econometrics to a misspecification test derived directly from chaos theory—the BDS test, developed by Brock, Dechert, and Scheinkman (1987). A spatial variant of the BDS test is introduced and applied in the context of two examples of spatial process models, one of which is concerned with the spatial distribution of regional investments in The Netherlands, the other with spatial crime patterns in Columbus, Ohio.     

A critical review of entrepreneurial ecosystems research: towards a future research agenda

The entrepreneurial ecosystem (EE) literature has attracted much attention, especially in policy circles. However, the concept suffers from a number of shortcomings: (1) it lacks a clear analytical framework that makes explicit what is cause and what is effect in an EE; (2) while being a systemic concept, the EE has not yet fully exploited insights from network theory, and it is not always clear in what way the proposed elements are connected in an EE; (3) it remains a challenge what institutions (and at what spatial scale) impact on the structure and performance of EE; (4) studies have often focused on the EE in single regions or clusters, but lack a comparative and multi-scalar perspective and (5) the EE literature tends to provide a static framework taking a snapshot of EE without considering systematically their evolution over time. For each of these shortcomings, we make a number of suggestions to take up in future research on EE.     

国外高速铁路空间经济效应研究进展及启示

国外高铁建设起步较早,发展相对成熟,而国内的相关研究则严重落后于建设实践。在对国外大量文献进行梳理的基础上,以高铁诱发的空间经济效应为切入点,将国外评估高铁空间经济效应的理论与方法归纳为成本-效益、新经济地理学、可达性、可计算一般均衡与Logit离散选择模型五类,认为从研究内容与方法等方面来看,国外的研究主要围绕着多维空间尺度效应、受影响的经济部门、站点周围土地利用形式、与其他交通方式之间关系以及总体效应等方面。最后指出可从实地调查、案例对比及综合研究等方面强化国内的相关研究。     

Buddhist buildings in England: the construction of ‘under-represented’ faith heritage in a multicultural and post-Christian setting

Until recently the ‘heritage industry’ in England overlooked buildings of minority faith traditions. Little has been written about this ‘under-represented’ heritage. Drawing on data from the first national survey of Buddhist buildings in England, we examine the ways in which Buddhist heritage is beginning to be incorporated into the state-funded ‘heritage industry’ as well as how Buddhist communities in England construct heritage through these buildings. First, we draw upon spatial theory in the study of religion to examine three dimensions of minority faith buildings in England and what this tells us about the communities involved: ‘location’ (i.e. the geographical location of the buildings); ‘space’ (i.e. what the buildings are used for and their relationship to local, national and transnational scales); and ‘place’ (i.e. what types of buildings are selected by different communities and why). We then turn to theories of memory that have become popular within the study of religion as well as heritage studies. Religion understood as ‘a chain of memory’ plays an important role in heritage construction via faith buildings, and an analysis of faith buildings, their spatial dimensions and role in ‘memorywork’, helps us think through the dynamics of modern religious belief in a multicultural and post-Christian setting.     

What “Is” Territorial Cohesion? What Does It “Do”?: Essentialist Versus Pragmatic Approaches to Using Concepts

The question, “what is territorial cohesion” has reverberated through European spatial policy since the publication of the European Spatial Development Perspective in 1999. Over the last 10 years, the European Spatial Policy Observation Network (ESPON) has made many efforts to define and measure the concept of “territorial cohesion”. Many such attempts assume that a policy concept must be defined in order to be “operationalized”. Or, in other words, that we must determine what the concept is before we can determine what it can or should do. This paper challenges this assumption in two parts. In the first, I review a number of ESPON projects to show how complex and uncertain these essentialist definitions have become. In the second, I analyse a number of national, regional and local government responses to the 2008 Green paper. I show that, whilst a clear and coherent definition has not been established, this concept is already operationalized in different policy frameworks. Bringing this together, I argue that users of such concepts ought to approach the issue differently, through a pragmatic line of enquiry: one that asks what territorial cohesion does, what it might do and how it might affect what other concepts, practices and materials do.     

EFFICIENT ALGORITHMS FOR CONSTRUCTING PROPER HIGHER ORDER SPATIAL LAG OPERATORS*

ABSTRACT. This paper extends the work of Blommestein and Koper (1992)–BK–on the construction of higher-order spatial lag operators without redundant and circular paths. For the case most relevant in spatial econometrics and spatial statistics, i.e., when contiguity between two observations (locations) is defined in a simple binary fashion, some deficiencies of the BK algorithms are outlined, corrected and an improvement suggested. In addition, three new algorithms are introduced and compared in terms of performance for a number of empirical contiguity structures. Particular attention is paid to a graph theoretic perspective on spatial lag operators and to the most efficient data structures for the storage and manipulation of spatial lags. The new forward iterative algorithm which uses a list form rather than a matrix to store the spatial lag information is shown to be several orders of magnitude faster than the BK solution. This allows the computation of proper higher-order spatial lags “on the fly” for even moderately large data sets such as 3,111 contiguous U. S. counties, which is not practical with the other algorithms.     

Arms Racing in ‘Space’: Spatial Modelling of Military Spending around the World

This paper builds on previous research into determinants of military spending by examining global and local spatial effects. Other research has examined the effects of regional differences and neighbours' spending levels with standard econometric techniques. This paper uses spatial econometrics to gain a better understanding of the influence of location and distance on levels of defence spending. I find that a spatial lag specification provides much more robust evidence of arms racing and security dilemma dynamics than previous studies. These basic dynamics have been difficult to detect empirically without the context and nuance introduced by spatial modelling. The paper represents a first cut at the topic, but two specific findings emerge. First, globally there is positive spatial correlation (nearby states have similar levels of spending). This conforms to arms racing and security dilemma expectations. And, second, locally there is variation in the patterns of spatial clustering across broad international regions (e.g. Europe, Asia etc.). The second finding supports previous research suggesting important qualitative regional variation in patterns of defence spending and international conflict. The models also confirm the effects of political regime type and interstate war on defence spending, and are robust to the inclusion of a temporally lagged dependent variable.