Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Micro-Level Data

Bell, Kathleen P.; Bockstael, Nancy E.

doi:10.1162/003465300558641

Cited by 277 publications

(180 citation statements)

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“…The former models are often referred to as spatial error models, the latter as spatial lag models. For empirical applications of such models in economics, see, for instance, Case (1991), Pinske and Slade (1998), Bell and Bockstael (2000).…”

Section: Introductionmentioning

confidence: 99%

Power Properties of Invariant Tests for Spatial Autocorrelation in Linear Regression

Martellosio

2009

Econom. Theory

153

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This paper derives some exact power properties of tests for spatial autocorrelation in the context of a linear regression model. In particular, we characterize the circumstances in which the power vanishes as the autocorrelation increases, thus extending the work of Krämer (2005, Journal of Statistical Planning and Inference 128, 489-496). More generally, the analysis in the paper sheds new light on how the power of tests for spatial autocorrelation is affected by the matrix of regressors and by the spatial structure. We mainly focus on the problem of residual spatial autocorrelation, in which case it is appropriate to restrict attention to the class of invariant tests, but we also consider the case when the autocorrelation is due to the presence of a spatially lagged dependent variable among the regressors. A numerical study aimed at assessing the practical relevance of the theoretical results is included.

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Section: Introductionmentioning

confidence: 99%

Power Properties of Invariant Tests for Spatial Autocorrelation in Linear Regression

Martellosio

2009

Econom. Theory

153

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“…3 Classic references on spatial models are Ord (1973, 1981), Anselin (1988), and Cressie (1993). 4 Some applications along these lines are, e.g., Audretsch and Feldmann (1996), Bell and Bockstael (2000), Bernat (1996), Besley and Case (1995), Bollinger and Ihlanfeldt (1997), Buettner (1999), Case (1991), Case, Hines, and Rosen (1993), Dowd and LeSage (1997), Holtz-Eakin (1994), Kelejian and Robinson (2000, 1997, 1993), Pinkse, Slade, and Brett (2002), Rey and Boarnet (1998), Shroder (1995), and Vigil (1998). 5 Recent theoretical contributions include Li (2004, 2001a,b), Baltagi, Song and Koh (2003), Conley (1999), , 2001, 1997, Kelejian, Prucha, and Yuzefovich (2003), Lee (2004Lee ( , 2003Lee ( , 2002Lee ( , 2001a, LeSage (2000LeSage ( , 1997, Pace and Barry (1997), Pinkse and Slade (1998), Pinkse, Slade, and Brett (2002), and Rey and Boarnet (2004).…”

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confidence: 99%

HAC estimation in a spatial framework

Kelejian

Prucha

2007

Journal of Econometrics

325

279

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We suggest a nonparametric heteroscedasticity and autocorrelation consistent (HAC) estimator of the variance-covariance (VC) matrix for a vector of sample moments within a spatial context. We demonstrate consistency under a set of assumptions that should be satisfied by a wide class of spatial models. We allow for more than one measure of distance, each of which may be measured with error. Monte Carlo results suggest that our estimator is reasonable in finite samples. We then consider a spatial model containing various complexities and demonstrate that our HAC estimator can be applied in the context of that model. Jel classification: C12, C14,C21

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“…Hence, the spatial econometric estimations are usually estimated by Maximum Likelihood (Anselin, 1988;Anselin and Hudak, 1992;Elhorst, 2010) or by GMM (Kelejian andPrucha, 1998, 1999;Bell and Bockstael, 2000). There are two predominant approaches to specifying the spatial model: One can either include a spatially weighted dependent variable (the so-called "spatial lag model") or a spatially autocorrelated error ("spatial error model") into the regression model.…”

Section: Main Econometric Issues and Potential Solutionsmentioning

confidence: 99%

Econometric Evaluation of EU Cohesion Policy: A Survey

Hagen

Mohl²

2016

Empirical Evidence on the Macroeconomic Effects of EU Cohesion Policy

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.Download this ZEW Discussion Paper from our ftp server:ftp://ftp.zew.de/pub/zew-docs/dp/dp09052.pdf Non-technical summaryMore than one third of the total EU budget is spent on so-called Cohesion Policy via the structural funds. Its main purpose is to reduce disparities among EU regions and to promote economic growth and convergence. Therefore, the convergence process of EU regions is a question of high political importance. The overall empirical evidence points to a small convergence effect of all or at least some European regions. However, whether or not this potential success results from EU Cohesion Policy remains an open question. The existing empirical evidence has provided mixed, if not contradictory, results. While some authors do find evidence of a positive impact of structural funds on economic growth, others find little to no impact at all. Against this background, this paper provides a fundamental review of the econometric evaluation of EU Cohesion Policy in order to shed light on the reasons for the diverging results. It has been shown that the econometric evaluation of EU Cohesion Policy is hampered by several econometric issues. Based on these issues we discuss potential solutions on how to cope with these problems and discuss the related literature.The most that can be concluded from empirical studies using country-level data is that Cohesion Policy seems to be only conditionally effective. Given a good quality institutional setup or decentralised governmental structures, Cohesion Policy has a positive impact on growth. However, using regional level data might be the preferable alternative because, first, EU Cohesion Policy focuses on the development and convergence of regions and, second, the robustness of the results is increased by the higher number of cross sections. The majority of the studies based on EU regions find at least a weak positive effect.One explanation for the weak results might be the fact that almost all studies are derived from a neoclassical growth model assuming that EU Cohesion Policy increases investments, and ultimately raising the economic growth rate. However, there is some empirical evidence that Cohesion Policy may only have a modest impact on investments. Moreover, we know very little about the labour market impact of EU Cohesion Policy. Hence, one task for future studies will be to investigate more thoroughly the channels through i which EU Cohesion Policy works.Another reason for the inconclusive empirical results m...

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Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Micro-Level Data

Cited by 277 publications

References 13 publications

Power Properties of Invariant Tests for Spatial Autocorrelation in Linear Regression

Power Properties of Invariant Tests for Spatial Autocorrelation in Linear Regression

HAC estimation in a spatial framework

Econometric Evaluation of EU Cohesion Policy: A Survey

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