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Journal ArticleDOI

Spatial Externalities, Spatial Multipliers, And Spatial Econometrics

01 Apr 2003-International Regional Science Review (SAGE Publications)-Vol. 26, Iss: 2, pp 153-166
TL;DR: In this paper, a taxonomy of spatial econometric model specifications that incorporate spatial externalities in various ways is presented, where the point of departure is a reduced form in which local or globa...
Abstract: This article outlines a taxonomy of spatial econometric model specifications that incorporate spatial externalities in various ways. The point of departure is a reduced form in which local or globa...
Citations
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Journal ArticleDOI
TL;DR: A number of conceptual issues pertaining to the implementation of an explicit "spatial" perspective in applied econometrics are reviewed, both from a theory-driven as well as from a data-driven perspective.

1,250 citations

Book ChapterDOI
01 Jan 2008
TL;DR: Spatial econometrics is a subfield of economic analysis that deals with the incorporation of spatial effects in econometric methods (Anselin, 1988a) as mentioned in this paper, where the structure of the dependence can somehow be related to location and distance, both in a geographic space as well as in a more general economic or social network space.
Abstract: Spatial econometrics is a subfield of econometrics that deals with the incorporation of spatial effects in econometric methods (Anselin, 1988a). Spatial effects may result from spatial dependence, a special case of cross-sectional dependence, or from spatial heterogeneity, a special case of cross-sectional heterogeneity. The distinction is that the structure of the dependence can somehow be related to location and distance, both in a geographic space as well as in a more general economic or social network space. Originally, most of the work in spatial econometrics was inspired by research questions arising in regional science and economic geography (early reviews can be found in, among others, Paelinck and Klaassen, 1979, Cliff and Ord, 1981, Upton and Fingleton, 1985, Anselin, 1988a, Haining, 1990, Anselin and Florax, 1995). However, more recently, spatial (and social) interaction has increasingly received more attention in mainstream econometrics as well, both from a theoretical as well as from an applied perspective (see the recent reviews and extensive references in Anselin and Bera, 1998, Anselin, 2001b, Anselin, 2002, Florax and Van Der Vlist, 2003, and Anselin et al., 2004a).

698 citations


Cites background or methods from "Spatial Externalities, Spatial Mult..."

  • ...…in spatial effects, the observations will be stacked as successive cross-sections for t = 1, . . . , T , referred to as yt (a N × 1 vector of cross-sectional observations for time period t), Xt (a N ×K matrix of observations on a cross-section of the explanatory variables for time period t) and t...

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  • ...In analogy to time series analysis, the two most commonly used models for spatial processes are the autoregressive and the moving average (for extensive technical discussion, see Anselin, 1988a, Anselin and Bera, 1998, Anselin, 2003, and the references cited therein)....

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  • ...…panel data contexts.4 Since most econometric aspects of spatial heterogeneity can be handled by means of the standard panel data methods, we will focus the discussion that 4 follows on spatial dependence and will only consider the heterogeneity when it is relevant to the modeling of the dependence....

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  • ...We close with some concluding remarks....

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  • ...The consequence is a so-called spatial multiplier (Anselin, 2003) which formally specifies how the joint determination of the values of the dependent variables in the spatial system is a function of the explanatory variables and error terms at all locations in the system....

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Journal ArticleDOI
TL;DR: In this article, the performance of three different simultaneous autoregressive (SAR) model types (spatial error = SAR err, lagged = SAR lag and mixed = SAR mix ) and common ordinary least squares (OLS) regression when accounting for spatial autocorrelation in species distribution data using four artificial data sets with known (but different) spatial auto-correlation structures.
Abstract: Aim Spatial autocorrelation is a frequent phenomenon in ecological data and can affect estimates of model coefficients and inference from statistical models. Here, we test the performance of three different simultaneous autoregressive (SAR) model types (spatial error = SAR err , lagged = SAR lag and mixed = SAR mix ) and common ordinary least squares (OLS) regression when accounting for spatial autocorrelation in species distribution data using four artificial data sets with known (but different) spatial autocorrelation structures. Methods We evaluate the performance of SAR models by examining spatial patterns in model residuals (with correlograms and residual maps), by comparing model parameter estimates with true values, and by assessing their type I error control with calibration curves. We calculate a total of 3240 SAR models and illustrate how the best models [in terms of minimum residual spatial autocorrelation (minRSA), maximum model fit ( R 2 ), or Akaike information criterion (AIC)] can be identified using model selection procedures. Results Our study shows that the performance of SAR models depends on model specification (i.e. model type, neighbourhood distance, coding styles of spatial weights matrices) and on the kind of spatial autocorrelation present. SAR model parameter estimates might not be more precise than those from OLS regressions in all cases. SAR err models were the most reliable SAR models and performed well in all cases (independent of the kind of spatial autocorrelation induced and whether models were selected by minRSA, R 2 or AIC), whereas OLS, SAR lag and SAR mix models showed weak type I error control and/or unpredictable biases in parameter estimates. Main conclusions SAR err models are recommended for use when dealing with spatially autocorrelated species distribution data. SAR lag and SAR mix might not always give better estimates of model coefficients than OLS, and can thus generate bias. Other spatial modelling techniques should be assessed comprehensively to test their predictive performance and accuracy for biogeographical and macroecological research.

685 citations


Cites methods from "Spatial Externalities, Spatial Mult..."

  • ...We first simulated three artificial data sets, which correspond to the mathematical formulation of the three model types for spatial externalities (Anselin, 2003)....

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Journal ArticleDOI
01 Mar 2010
TL;DR: It is argued that the field of spatial econometric methodology has moved from the margins to the mainstream of applied econometrics and social science methodology during the past 30 years.
Abstract: In this paper, I give a personal view on the development of the field of spatial econometrics during the past 30 years. I argue that it has moved from the margins to the mainstream of applied econometrics and social science methodology. I distinguish three broad phases in the development, which I refer to as preconditions, take off and maturity. For each of these phases I describe the main methodological focus and list major contributions. I conclude with some speculations about future directions. Resumen En este articulo, expongo mi opinion personal sobre el avance en el campo de la econometria espacial durante los ultimos 30 anos. Mi argumento es que ha pasado de estar en la periferia de la econometria espacial y la metodologia de ciencias sociales a ser algo corriente. Hago la distincion entre tres fases principales en el avance, a las que denomino precondiciones, arranque y madurez. Para cada una de estas fases describo el objetivo metodologico principal y proporciono un listado con las contribuciones principales. Concluyo con especulaciones sobre posibles direcciones en el futuro.

621 citations


Cites background from "Spatial Externalities, Spatial Mult..."

  • ...Attention focuses on model specifications other than the familiar spatial lag and spatial autoregressive error models as well, such as a general framework to deal with spatial externalities outlined in Anselin (2003)....

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  • ...…be included for the dependent variable (leading to so-called spatial lag models), explanatory variables (spatial cross-regressive models) and error terms (spatial error models), as well as combinations of these, yielding a rich array of spatially explicit models (see, for example, Anselin 2003)....

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Journal ArticleDOI
TL;DR: The theoretical understanding and empirical estimation of “neighborhood effects” on health are bolstered by collecting data on more causally proximate social processes and by taking into account spatial interdependencies among neighborhoods.
Abstract: This study addresses two questions about why neighborhood contexts matter for individuals via a multilevel, spatial analysis of birth weight for 101,662 live births within 342 Chicago neighborhoods. First, what are the mechanisms through which neighborhood structural composition is related to health? The results show that mechanisms related to stress and adaptation (violent crime, reciprocal exchange and participation in local voluntary associations) are the most robust neighborhood‐level predictors of birth weight. Second, are contextual influences on health limited to the immediate neighborhood or do they extend to a wider geographic context? The results show that contextual effects on birth weight extend to the social environment beyond the immediate neighborhood, even after adjusting for potentially confounding covariates. These findings suggest that the theoretical understanding and empirical estimation of “neighborhood effects” on health are bolstered by collecting data on more causally proximate so...

493 citations

References
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Book
01 Jan 1991
TL;DR: In this paper, the authors present a survey of statistics for spatial data in the field of geostatistics, including spatial point patterns and point patterns modeling objects, using Lattice Data and spatial models on lattices.
Abstract: Statistics for Spatial Data GEOSTATISTICAL DATA Geostatistics Spatial Prediction and Kriging Applications of Geostatistics Special Topics in Statistics for Spatial Data LATTICE DATA Spatial Models on Lattices Inference for Lattice Models SPATIAL PATTERNS Spatial Point Patterns Modeling Objects References Author Index Subject Index.

8,631 citations

Book
Luc Anselin1
31 Aug 1988
TL;DR: In this article, a typology of Spatial Econometric Models is presented, and the maximum likelihood approach to estimate and test Spatial Process Models is proposed, as well as alternative approaches to Inference in Spatial process models.
Abstract: 1: Introduction.- 2: The Scope of Spatial Econometrics.- 3: The Formal Expression of Spatial Effects.- 4: A Typology of Spatial Econometric Models.- 5: Spatial Stochastic Processes: Terminology and General Properties.- 6: The Maximum Likelihood Approach to Spatial Process Models.- 7: Alternative Approaches to Inference in Spatial Process Models.- 8: Spatial Dependence in Regression Error Terms.- 9: Spatial Heterogeneity.- 10: Models in Space and Time.- 11: Problem Areas in Estimation and Testing for Spatial Process Models.- 12: Operational Issues and Empirical Applications.- 13: Model Validation and Specification Tests in Spatial Econometric Models.- 14: Model Selection in Spatial Econometric Models.- 15: Conclusions.- References.

8,282 citations


"Spatial Externalities, Spatial Mult..." refers background in this paper

  • ...For an extensive discussion of spatial weights, see Cliff and Ord (1981) and Anselin (1988)....

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  • ...The standard taxonomy of spatial autoregressive lag and error models commonly applied in spatial econometrics (Anselin 1988) is perhaps too simplistic and leaves out other interesting possibilities for mechanisms through which phenomena or actions at a given location affect actors and properties at…...

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Journal ArticleDOI
TL;DR: Cressie et al. as discussed by the authors presented the Statistics for Spatial Data (SDS) for the first time in 1991, and used it for the purpose of statistical analysis of spatial data.
Abstract: 5. Statistics for Spatial Data. By N. Cressie. ISBN 0 471 84336 9. Wiley, Chichester, 1991. 900 pp. £71.00.

5,555 citations

Journal ArticleDOI
TL;DR: The authors examined the reflection problem that arises when a researcher observing the distribution of behaviour in a population tries to infer whether the average behaviour in some group influences the behaviour of the individuals that comprise the group.
Abstract: This paper examines the reflection problem that arises when a researcher observing the distribution of behaviour in a population tries to infer whether the average behaviour in some group influences the behaviour of the individuals that comprise the group. It is found that inference is not possible unless the researcher has prior information specifying the compisition of reference groups. If this information is available, the prospects for inference depend critically on the population relationship between the variables defining reference groups and those directly affecting outcomes. Inference is difficult to implossible if these variables are functionally dependent or are statistically independent. The prospects are better if the variables defining reference groups and those directly affecting outcomes are moderately related in the population.

4,495 citations

Book
01 Feb 1981
TL;DR: The authors describe various ways the degree of spatial autocorrelation in a set of variate values can be assessed and to which the pattern formed by the location of objects treatable as points can be examined.
Abstract: Describing the various ways the degree of spatial autocorrelation in a set of variate values can be assessed and to which the pattern formed by the location of objects treatable as points can be examined.

2,801 citations

Trending Questions (1)
Does a spatial externality between the municipality and the citizen arise as a result of residential suburbanization?

The article discusses different spatial econometric model specifications that incorporate spatial externalities, but it does not specifically address the question of whether a spatial externality arises as a result of residential suburbanization.