Multi-dimensional Spatial Auto-regressive Models: How do they Perform in an Economic Growth Framework? (original) (raw)
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The empirical literature about economic growth has usually ignored spatial interdependence among countries. This paper uses spatial econometrics to estimate a growth model that includes crosscountry interdependence, in which a country's economic growth depends on the growth rate of its neighbors. Based on a sample of 98 countries over three decades (1965-75, 1975-85, 1985-95) we find that spatial relationships across countries are quite relevant. A country's economic growth is indeed affected by the performance of its neighbors and then influenced by its own geographical position. This result suggests that the spillover effects among countries are important for growth. Our results indicate that spatial interrelation can not be ignored in the analysis of economic growth. Ignoring such relationships can result in model misspecification.
Using Spatial Panel Data in Modelling Regional Growth and Convergence
2005
Analyses (ISAE) hosts the preliminary results of the research projects carried out within ISAE. The diffusion of the papers is subject to the favourable opinion of an anonymous referee, whom we would like to thank. The opinions expressed are merely the Authors' own and in no way involve the ISAE responsability.
A Simultaneous Spatial Panel Data Model of Regional Growth Variation: An Empirical Analysis of …
Working …, 2008
In this paper we develop a spatial panel simultaneous-equations model of employment growth, migration behavior, local public services and median household income in a partial lag-adjustment growth-equilibrium framework and utilizing a one-way error component model for the disturbances. To estimate the model, we developed a five-step new estimation strategy by generalizing the Generalized Spatial Three-Stage Least Squares (GS3SLS) approach outlined in Kelejian and Prucha into a panel data setting. The empirical implementation of the model uses county-level data from the 418 Appalachian counties for 1980-2000. The estimates show the existence of feedback simultaneities among the endogenous variables of the model, the existence of conditional convergence with respect to the respective endogenous variable of each equation of the model, and the existence of spatial autoregressive lag effects and spatial cross-regressive lag effects with respect to the endogenous variables of the model. Moreover, the speed of adjustment parameters is generally comparable to those in literature. One of the key conclusions is that sector-specific policies should be integrated and harmonized in order to give the desirable outcome. In addition, regionally focusing resources for development policy may yield greater returns than treating all locations the same.
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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...
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,
Applied Spatial Econometrics: Raising the Bar
Spatial Economic Analysis, 2010
This paper places the key issues and implications of the new 'introductory' book on spatial econometrics by James LeSage & Kelley Pace (2009) in a broader perspective: the argument in favour of the spatial Durbin model, the use of indirect effects as a more valid basis for testing whether spatial spillovers are significant, the use of Bayesian posterior model probabilities to determine which spatial weights matrix best describes the data, and the book's contribution to the literature on spatiotemporal models. The main conclusion is that the state of the art of applied spatial econometrics has taken a step change with the publication of this book.
Modelling spatial externalities in panel data: The Spatial Durbin model revisited*
Papers in Regional Science, 2011
The purpose of this paper is twofold. First, it extends the spatial Durbin model to panel data allowing for non-spherical disturbances and proposes two alternative estimators based on ML techniques. While one of the estimators exhibits more degrees of freedom, the other is computationally less burdensome. Results from a Monte Carlo study reveal that both estimators have satisfactory small sample properties also in cases when the error structure is in effect spherical. Second, the paper demonstrates that conventional testing procedures may wrongly reject the existence of spatial externalities. In particular, it shows that the incidence of a type II error increases as the spatial weight matrix becomes denser.
Modeling Spatial Externalities: A Panel Data Approach
SSRN Electronic Journal, 2010
In this paper we argue that the Spatial Durbin Model (SDM) is an appropriate framework to empirically quantify different kinds of externalities. Besides, it is also attractive from an econometric point of view as it nests several other models frequently employed. Up to now the SDM was applied in cross-sectional settings only, thereby ignoring individual heterogeneity. This paper extends the SDM to panel data allowing for non-spherical disturbances and proposes an estimator based on ML techniques. Results from a Monte Carlo study reveal that the estimator has satisfactory small sample properties and that neglecting the non-spherical nature of the errors leads to inflated standard errors. Moreover, we show that the incidence of type two errors in testing procedures for parameter significance of spatially lagged variables is the higher the denser the spatial weight matrix.