Spatial Econometrics Literature Research Articles

On model specification and parameter space definitions in higher order spatial econometric models

Higher-order spatial econometric models that include more than one weights matrix have seen increasing use in the spatial econometrics literature. There are two distinct issues related to the specification of these extended models. The first issue is what form the higher-order spatial econometric model takes, i.e. higher-order polynomials in the spatial weights matrices vs. higher-order spatial autoregressive processes. The second issue relates to the parameter space in such models and how this can affect the choice of model specification, estimation, and inference. We outline a procedure that is simple both mathematically and computationally for finding the stationary region for spatial econometric models with up to K weights matrices for higher-order spatial autoregressive processes. We also compare and contrast this approach with the parameter space for models that incorporate higher-order polynomials in the spatial weights matrices. Regardless of the model utilized in empirical practice, ignoring the relevant parameter region can lead to incorrect inferences regarding both the nature of the spatial autocorrelation process and the effects of changes in covariates on the dependent variable.

‘DUAL’ GRAVITY: USING SPATIAL ECONOMETRICS TO CONTROL FOR MULTILATERAL RESISTANCE

SUMMARYWe derive a quantity‐based structural gravity equation system in which both trade flows and error terms are cross‐sectionally correlated. This system can be estimated using techniques borrowed from the spatial econometrics literature. To illustrate our methodology, we apply it to a well‐known Canada–US trade dataset. We find that border effects between the USA and Canada are smaller than suggested by previous studies: about 7.5 for Canadian provinces and about 1.3 for US states. Hence controlling directly for cross‐sectional interdependence among both trade flows and error terms reduces measured border effects by capturing ‘multilateral resistance’. Copyright © 2010 John Wiley & Sons, Ltd.

THE FUTURE OF SPATIAL ECONOMETRICS

ABSTRACT The purpose of this paper is threefold. First, we give an overview of the general direction the spatial econometrics literature has taken without attempting to provide a representative survey of all interesting work that has appeared. Second, we identify a number of problems in spatial econometrics that are as yet unresolved. Finally, we provide advocacy for the notion that new spatial econometric theory should be inspired by actual empirical applications as opposed to being directed by what appears to be the most obvious extension of what is currently available.

New spatial econometric techniques and applications in regional science

The papers appearing in this special issue of Papers in Regional Science, which is devoted to spatial econometrics, come from the First International Conference of the Spatial Econometrics Association held in Cambridge (UK) 12-14 July 2008. This conference was the first official meeting of the new association, which was established in May 2006 in Rome and which has already attracted more than 150 members from around the world. At the Cambridge conference there were close to 120 delegates and more than 100 papers were presented. With regard to the eight papers appearing in this special issue, we would particularly like to thank the authors and the referees for their contribution to what we believe is an interesting and lively selection. Recent years have seen a real explosion in the application of spatial statistical models in all branches of social sciences and in particular in economics. Spatial econometrics models have been used to analyse different topics (see for example Anselin et al. 2004 for a review) and as a matter of fact spatial regression techniques are now becoming an established component in the applied econometrics toolbox, as witnessed by the increasing attention given to this topic in standard econometrics textbooks (Maddala 2001; Woolridge 2002; Gujarati 2003; Kennedy 2003; Baltagi 2008).

The Spatial Probit Model of Interdependent Binary Outcomes: Estimation, Interpretation, and Presentation

We have argued and shown elsewhere the ubiquity and prominence of spatial interdependence, i.e., interdependence of outcomes among cross-sectional units, across the theories and substance of political and social science, and we have noted that much previous practice neglected this interdependence or treated it solely as nuisance, to the serious detriment of sound inference. These earlier studies considered only linear-regression models of spatial/spatio-temporal interdependence. For those classes of models, we (1) derived analytically in simple cases the biases of non-spatial and spatial least-squares (LS) under interdependence, (2) explored in simulations under richer, more realistic circumstances the properties of the biased non-spatial and spatial least-squares estimators and of the consistent and asymptotically efficient spatial method-of-moments (i.e., IV, 2SLS, GMM) and spatial maximum-likelihood estimators (ML), and (3) showed how to calculate, interpret, and present effectively the estimated spatial/spatio-temporal effects and dynamics of such models, along with appropriate standard errors, confidence intervals, hypothesis tests, etc. This paper begins a like set of tasks for binary-outcome models. We start again by stressing the ubiquity and centrality substantively and theoretically of interdependence in binary outcomes of interest to political and social scientists. We note that, again, this interdependence has typically been ignored in most contexts where it likely arises and that, in the few contexts where it has been acknowledged, or even rather centrally emphasized, those of policy diffusion and of social networks, the endogeneity of the spatial lag used (appropriately) to model the interdependence has only rarely been recognized. Next, we note and explain some of the severe challenges for empirical analysis posed by spatial interdependence in binary-outcome models, and then we follow recent advances in the spatial-econometric literature to suggest Bayesian or recursive-importance-sampling (RIS) approaches for tackling the estimation demands of these models. In brief and in general, the estimation complications arise because among the RHS variables is an endogenous weighted spatial-lag of the unobserved latent outcome, y*, in the other units; Bayesian or RIS techniques facilitate the complicated nested optimization exercise that follows from that fact. We show how to calculate estimated spatial effects (as opposed to parameter estimates) in such models and how to construct confidence regions for those, adopting simulation strategies for these purposes, and then how to present such estimates effectively.

Social Interaction Effects in an Inter-Generational Model of Informal Care Giving

We study jointly the health perception of the elderly and the care giving decision of their adult children. Social interactions play a crucial role: elder parents' health perception depends on relations with household members. On the other hand adult children make their care giving decisions strategically, meaning that each of them considers his siblings' decision. We find empirical evidence which support this claim using the 2004 wave of the SHARE survey. We estimate social interaction effects by means of methods taken from the spatial econometric literature. Health perception relation with care giving depends on the determinants of adult children's decision to care: Parents' health may be modelled as a common good for parents and children; the latter's decision may be driven by bequest motives or by pure altruism and/or cultural values. We test implications of the model thanks to the unique features of the SHARE dataset: it is trans--national, allowing to control for cultural and institutional differences, it contains information on health status of over-50 Europeans and details on their social and intergenerational relations.

Spatial Competition and the Price of College

This article provides the first evidence that universities compete directly on price, and that the market for students depends on the proximity of competitors. Exploiting detailed data from private U.S. universities, price competition is tested by introducing geographic proximity into a spatial-autoregressive tuition model. Standard spatial models show that list and net tuition are inversely related to distance between institutions, consistent with price competition in higher education. An extension to the spatial-econometrics literature relaxes a constraint that estimated spatial relationships are common across all observations, implying that spatial effects differ across qualitative classes of institutions.

CGE EVALUATION OF A UNIVERSITY'S EFFECTS ON A REGIONAL ECONOMY: AN INTEGRATED ASSESSMENT OF EXPENDITURE AND KNOWLEDGE IMPACTS

In recent years many universities have commissioned studies of the effects of their institutions on their local economies. Typically, these impact studies have concentrated on the demand‐side stimuli to the regional economy that the university generates. Normally, the studies are undertaken with comparative‐static input‐output models. The present study employs a dynamic multiregional computable general equilibrium model to investigate supply‐side as well as demand‐side effects. There are a range of supply‐side effects that have been investigated in the spatial econometrics literature. The supply‐side impacts of the university that we examine in particular are a rise in the average skill level of the local workforce, and successful R&D outcomes. CGE modelling allows simulation of the associated productivity effects, while the dynamic features of the model allow for consequent effects on a region's population and capital stock growth rates to be taken into account.

Space and Growth: A Survey of Empirical Evidence and Methods

This paper reviews the empirical literature on growth and convergence that has addressed the importance of spatial factors. An important distinction in this literature is the one between absolute and relative location. The literature on absolute location predominantly uses non-spatial econometric techniques, and is strongly linked to the economic growth literature. In contrast, studies on relative location tend to be weakly linked to theory, but apply relatively sophisticated econometric techniques to account for spatial effects. Most studies of the latter type are regional in nature, although there is a growing interest in extending the analysis to a cross-country setting. Both regional and cross-country studies typically make use of so-called spatial process models. Rather than modelling the impediments of space and distance directly, spatial process models start from exogenously provided information about the spatial structure. Our review shows that the usage of simple spatial autoregressive lag and error models abounds in the spatial econometrics literature. We assess the appropriateness of such models, and identify areas of potential concern. The rather weak linkage between theory and operational models, the dominance of 'global' over 'local' spatial association patterns, and the implicit restrictions on spatial interaction induced by many of the habitual specifications of the spatial weights matrix concurrently constitute areas where improvements can be made.

A Comparison of Four Spatial Regression Models for Yield Monitor Data: A Case Study from Argentina

The gap between data analysis and site-specific recommendations has been identified as one of the key constraints on widespread adoption of precision agriculture technology. This disparity is in part due to the fact that analytical techniques available to understand crop GIS layers have lagged behind development of data gathering and storage technologies. Yield monitor, sensor and other spatially dense agronomic data is often autocorrelated, and this dependence among neighboring observations violates the assumptions of classical statistical analysis. Consequently, reliability of estimates may be compromised. Spatial regression analysis is one way to more fully exploit the information contained in spatially dense data. Spatial regression techniques can also adjust for bias and inefficiency caused by spatial autocorrelation. The objective of this paper is to compare four spatial regression methods that explicitly incorporate spatial correlation in the economic analysis of variable rate technology: (1) a regression approach adopted from the spatial econometric literature; (2) a polynomial trend regression approach; (3) a classical nearest neighbor analysis; and (4) a geostatistical approach. The data used in the analysis is from a variable rate nitrogen trial in the Cordoba Province, Argentina, 1999. The spatial regression approaches offered stronger statistical evidence of spatial heterogeneity of corn yield response to nitrogen than ordinary least squares. The spatial econometric analysis can be implemented on relatively small data sets that do not have enough observations for estimation of the semivariogram required by geostatistics. The nearest neighbor and polynomial trend analyses can be implemented with ordinary least squares routines that are available in GIS software. The main result of this study is that conclusions drawn from marginal analyses of this variable rate nitrogen trial were similar for each of the spatial regression models, although the assumptions about spatial process in each model are quite different.

Spatial Econometric Data Analysis: Moving Beyond Traditional Models

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.

Under the hood Issues in the specification and interpretation of spatial regression models

AbstractThis paper reviews a number of conceptual issues pertaining to the implementation of an explicit “spatial” perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theory‐driven as well as from a data‐driven perspective. Considerable attention is paid to the inferential framework necessary to carry out estimation and testing and the different assumptions, constraints and implications embedded in the various specifications available in the literature. The review combines insights from the traditional spatial econometrics literature as well as from geostatistics, biostatistics and medical image analysis.

Under the hood: Issues in the specification and interpretation of spatial regression models

This paper reviews a number of conceptual issues pertaining to the implementation of an explicit “spatial” perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theory-driven as well as from a data-driven perspective. Considerable attention is paid to the inferential framework necessary to carry out estimation and testing and the different assumptions, constraints and implications embedded in the various specifications available in the literature. The review combines insights from the traditional spatial econometrics literature as well as from geostatistics, biostatistics and medical image analysis.

Estimating Spatial Models Within A Disequilibrium Framework

Modeling demand in a spatial context requires careful handling of regional interactions. In situations where there are constraints in some markets that lead to spillovers to others it is useful to build this explicitly into the model. In this paper I present a theoretical model that is related to the disequilibrium and the spatial econometric literature. Under certain conditions the model may be estimable and an appropriate estimation technique that uses the EM algorithm, is put forward. A data set from the U.K. market for secondary school places provides a test for the procedure.

Regional economic dynamics and convergence in the European Union

This paper uses an alternative approach to convergence analysis, which, in our opinion, is richer than those traditionally proposed. We believe that the evolution of the whole regional distribution is what matters, not that of an average or representative economy, implicit in the or convergence concepts. Moreover, when analyzing inequality among regional economies, geographic space acquires a preeminent role. We therefore apply spatial association tests recently developed in the spatial econometrics literature and relate them to convergence analysis. Specifically, we apply this methodology to the analysis of regional dynamics and convergence in the European Union (EU). In this context, the progress made in the integration process has highlighted the question of economic disparities at regional level. The process of convergence came to an end in the late seventies, coinciding with major changes in economic activity. This paper shows how these factors appear to have provoked a regionally differentiated response which, though significant, did not, in all likelihood, exacerbate the decrease in regional inequalities.

Spatial aggregation, spatial dependence and predictability in the UK housing market

Abstract Most time‐series studies of UK housing markets are carried out on national data. However, housing markets may be better characterised as a series of interconnected sub‐national markets. In this paper, we use methods taken from the spatial econometrics literature and from cointegration to explore the nature of spatial interactions in UK regional house prices and housing starts. The three central spatial issues are the extent to which markets are (a) homogeneous (b) dependent and (c) convergent, across the English regions. We find that housing starts equations are remarkably similar across regions and hence construction can be characterised as a national market. There is a high degree of spatial dependence in terms of both house prices and starts; dependence is uni‐directional in the former, but not the latter. Finally, cointegration results suggest that regional prices converge to long‐run relationships, despite major short‐term variation.