Panel data models with spatially correlated error components

Kapoor, Mudit; Kelejian, Harry H.; Prucha, Ingmar R.

doi:10.1016/j.jeconom.2006.09.004

Cited by 592 publications

(614 citation statements)

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“…See Lee (2007), who explored this link formally, and Lee, Liu, and Lin (2010). 37 In addition, the spatial econometrics literature has made important advances in terms of allowing for spatial autocorrelation in error structures: see Kapoor, Kelejian, and Prucha (2007) and Kelejian and Prucha (2010) for recent examples of advances in the study of spatial environments under weak error assumptions and Anselin (2010) for a review of the area. Spatial econometrics models have a long tradition in geography where the weights attached to different observations are motivated in terms of various distance concepts.…”

Section: Spatial Econometrics Specifications Of Social Interactionsmentioning

confidence: 99%

Identification of Social Interactions

Blume

Brock²,

Durlauf³

et al. 2011

Handbook of Social Economics

274

224

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While interest in social determinants of individual behavior has led to a rich theoretical literature and many efforts to measure these influences, a mature "social econometrics" has yet to emerge. This chapter provides a critical overview of the identification of social interactions. We consider linear and discrete choice models as well as social networks structures. We also consider experimental and quasi-experimental methods. In addition to describing the state of the identification literature, we indicate areas where additional research is especially needed and suggest some directions that appear to be especially promising. KeywordsSocial interactions, social networks, identification JEL Classification C21, C23, C31, C35, C72, Z13 CommentsPrepared for the Handbook of Social Economics, J. Benhabib, A. Bisin, and M. Jackson, eds. We thank Andres Aradillas-Lopez, Jane Cooley, James Heckman, Charles Manski and Salvador Navarro for many conversations on the subject of this chapter. Financial support has been supplied to Blume by NSF grant CCF-0910940 and WWTF Grant "Die Evolution von Normen and Konventionen in der

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Section: Spatial Econometrics Specifications Of Social Interactionsmentioning

confidence: 99%

Identification of Social Interactions

Blume

Brock²,

Durlauf³

et al. 2011

Handbook of Social Economics

274

224

View full text Add to dashboard Cite

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“…7 Following Kapoor, Kelejian and Prucha (2007), our measure of dispersion is closely related to the standard measure of the RMSE, but it is based on quantiles rather than moments because, unlike moments, quantiles are assured to exit. For ease of presentation, Table 1 gives the results on the bias and RMSE of the ML estimators for the SUR parameters, the spatial lags and spatial errors coefficients for a SAR process.…”

Section: The Data Generating Processmentioning

confidence: 99%

“…Here, we focus on combining the spatial and panel aspects of the data in a SUR context. In fact, Anselin (1988) and Elhorst (2003) among others provided maximum likelihood (ML) methods that combine panel data with spatial analysis, while Kapoor, Kelejian and Prucha (2007) provided a generalized moments estimators (GM) approach for estimating a spatial random effects panel model with SAR disturbances. Fingleton (2008a) extended the GM approach of Kapoor, Kelejian and Prucha to allow for spatial moving average disturbances, see Anselin, Le Gallo and Jayet (2008) for a recent survey.…”

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confidence: 99%

Maximum likelihood estimation and Lagrange multiplier tests for panel seemingly unrelated regressions with spatial lag and spatial errors: An application to hedonic housing prices in Paris

Baltagi

Bresson

2011

Journal of Urban Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.IZA Discussion Paper No. 5227 September 2010 ABSTRACT Maximum Likelihood Estimation and Lagrange Multiplier Tests for Panel Seemingly Unrelated Regressions with Spatial Lag and Spatial Errors: An Application to Hedonic Housing Prices in Paris *This paper proposes maximum likelihood estimators for panel seemingly unrelated regressions with both spatial lag and spatial error components. We study the general case where spatial effects are incorporated via spatial errors terms and via a spatial lag dependent variable and where the heterogeneity in the panel is incorporated via an error component specification. We generalize the approach of Wang and Kockelman (2007) and propose joint and conditional Lagrange Multiplier tests for spatial autocorrelation and random effects for this spatial SUR panel model. The small sample performance of the proposed estimators and tests are examined using Monte Carlo experiments. An empirical application to hedonic housing prices in Paris illustrates these methods. The proposed specification uses a system of three SUR equations corresponding to three types of flats within 80 districts of Paris over the period [1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003]. We test for spatial effects and heterogeneity and find reasonable estimates of the shadow prices for housing characteristics. JEL Classification:C31, C33, R21

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“…For zero correlation q = 0, the SEM filter vanishes to B N T = I N T , and the GLS estimator in the SUR-SEM model reduces to the GLS type SUR estimator (8).…”

Section: Lesage and Pace (2009)mentioning

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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Panel data models with spatially correlated error components

Cited by 592 publications

References 49 publications

Identification of Social Interactions

Identification of Social Interactions

Maximum likelihood estimation and Lagrange multiplier tests for panel seemingly unrelated regressions with spatial lag and spatial errors: An application to hedonic housing prices in Paris

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

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