Spatial Panel Data Models

Elhorst, J. Paul

doi:10.1007/978-3-642-03647-7_19

Cited by 451 publications

(374 citation statements)

References 46 publications

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“…1 and 2 can be estimated using Maximum Likelihood (ML), General Method of Moments and Bayesian MCMC. Here we follow the procedure in Elhorst (2009) and use ML to estimate Eq. 2, where Eq.…”

Section: Returns To Scalementioning

confidence: 99%

“…Secondly, we use a row-normalised W which simpli…es the computation of ln jI N W j to evaluate the log-likelihoods. For more details on this see Elhorst (2009). Thirdly, we use the Within transformation to estimate the non-spatial and spatial models by demeaning in the cross-sectional dimension to circumvent the incidental parameter problem associated with the …xed e¤ects, which eliminates these e¤ects (and the intercept).…”

Section: Returns To Scalementioning

confidence: 99%

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Returns to scale and curvature in the presence of spillovers: evidence from European countries

2015

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Drawing on a recent development on the interpretation of spatial econometric models we extend two classic characteristics of production (returns to scale and diminishing marginal productivity of factor inputs) to the spatial case. In the context of a spatial translog production function we de…ne internal (i.e. direct), external (i.e. indirect) and total (direct plus indirect) returns to scale. The spatial production function gives rise to direct, indirect and total productions functions so we set out empirical checks to establish if these functions are concave. The ideas of spatial returns to scale and spatial concavity/convexity can easily be applied to other technologies (e.g. cost and distance functions) and other functional forms (e.g. Cobb-Douglas). We apply these ideas to aggregate production of European countries using balanced panel data for the period 1990 2011.

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“…1 and 2 can be estimated using Maximum Likelihood (ML), General Method of Moments and Bayesian MCMC. Here we follow the procedure in Elhorst (2009) and use ML to estimate Eq. 2, where Eq.…”

Section: Returns To Scalementioning

confidence: 99%

Section: Returns To Scalementioning

confidence: 99%

Returns to scale and curvature in the presence of spillovers: evidence from European countries

2015

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“…where E(ee ′ ) = Σ sur = Σ T ⊗ I N is the SUR covariance matrix (5), and the system spread matrix is…”

Section: Simple Gls Estimators Of the Sar-sur Modelmentioning

confidence: 99%

“…On the other hand, panel models have become increasingly important and different estimators in such models with spatial components have also been studied; see e.g. Kapoor et al (2007), Anselin et al (2008), Baltagi (2008), Elhorst (2010), and Lee and Yu (2010). It is clearly useful to examine the sensitivity of these estimators in terms of a minor change in the spatial correlation parameter ρ.…”

Section: Introductionmentioning

confidence: 99%

Spatial system estimators for panel models: A sensitivity and simulation study

Liu

Polasek

2014

Mathematics and Computers in Simulation

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System of panel models are popular models in applied sciences and the question of spatial errors has created the recent demand for spatial system estimation of panel models.Therefore we propose new diagnostic methods to explore if the spatial component will change significantly the outcome of non-spatial estimates of seemingly unrelated regression (SUR) systems. We apply a local sensitivity approach to study the behavior of generalized least squares (GLS) estimators in two spatial autoregression SUR system models: a SAR model with SUR errors (SAR-SUR) and a SUR model with spatial errors (SUR-SEM). Using matrix derivative calculus we establish a sensitivity matrix for spatial panel models and we show how a first order Taylor approximation of the GLS estimators can be used to approximate the GLS estimators in spatial SUR models. In a simulation study we demonstrate the good quality of our approximation results. KeywordsSeemingly unrelated regression models, panel systems with spatial errors, SAR and SEM models, generalized least-squares estimators, Taylor approximations JEL ClassificationG14, G15, C22

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“…Still other advances have involved the extension of GMM to a spatial moving average process, including an endogenous spatial lag, for spatially explicit panel data (Fingleton 2008) as well as variance covariance matrix specification (LeSage and Pace 2007). Spatial models for flows, for panels, and for latent variables are growing in use (Elhorst 2003(Elhorst , 2010Anselin, Le Gallo, and Jayet 2008).…”

Section: Spatial Econometricsmentioning

confidence: 99%

Progress in Spatial Demography

Matthews¹,

Parker²

2013

DemRes

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BACKGROUNDDemography is an inherently spatial science, yet the application of spatial data and methods to demographic research has tended to lag that of other disciplines. In recent years, there has been a surge in interest in adding a spatial perspective to demography. This sharp rise in interest has been driven in part by rapid advances in geospatial data, new technologies, and methods of analysis. OBJECTIVESWe offer a brief introduction to four of the advanced spatial analytic methods: spatial econometrics, geographically weighted regression, multilevel modeling, and spatial pattern analysis. We look at both the methods used and the insights that can be gained by applying a spatial perspective to demographic processes and outcomes. To help illustrate these substantive insights, we introduce six papers that are included in a Special Collection on Spatial Demography. We close with some predictions for the future, as we anticipate that spatial thinking and the use of geospatial data, technology, and analytical methods will change how many demographers address important demographic research questions.

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Spatial Panel Data Models

Cited by 451 publications

References 46 publications

Returns to scale and curvature in the presence of spillovers: evidence from European countries

Returns to scale and curvature in the presence of spillovers: evidence from European countries

Spatial system estimators for panel models: A sensitivity and simulation study

Progress in Spatial Demography

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