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Testing for Cross-sectional Dependence in Regional Panel Data

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  • Peter Sandholt Jensen
  • Torben Dall Schmidt

Abstract

We examine three tests of cross-sectional dependence and apply them to a Danish regional panel dataset with few time periods and a large cross-section: the CD test due to Pesaran ( 2004 ), the Schott test and the Liu--Lin--Shao test. We show that the CD test and the Schott test have good properties in a Monte Carlo study. When controlling for panel-specific and time-specific fixed effects, the Schott test is superior. Our application shows that there is cross-sectional dependence in regional employment growth across Danish regions. We also show that this dependence can be accounted for by time-specific fixed effects. Thus, the tests uncover new properties of the regional data. RÉSUMÉ Nous examinons trois tests d'autonomie transversale, que nous appliquons à des ensembles de données d'une commission régionale danoise, avec peu de plages de temps et un vaste échantillons: le test d'autonomie transversale mené par Pesaran (2004), le test de Schott et le test Liu--Lin--Shao. Nous démontronsque le test d'autonomie transversale et le test de Schott présentent de bonnes propriétés dans uneétude Monte Carlo. Lorsquel'on se penchesur les effets propres au panel et les effets fixes en fonction du temps, le test de Schott dépasse les autres. Notre application illustre le dépendance transversale dans l'expansion de l'emploi à l’échelon régional dans les régions danoises, et nous démontronsque cette dépendance peut s'expliquer par des effets fixes en fonction du temps. De cette façon, le test révèle des propriétés nouvelles des données régionales. RESUMEN Examinamos tres pruebas de dependencia transversal y las aplicamos a un conjunto de datos de panel regional en Dinamarca con pocos períodos de tiempo y una gran sección transversal: la prueba CD de Pesaran (2004), la prueba Schott y la prueba Liu--Lin--Shao. Demostramos que la prueba CD y la prueba Schott poseen buenas propiedades en un estudio de Monte Carlo. Al controlar los efectos fijos específicos de panel y específicos de tiempo, la prueba Schott es superior. Nuestra aplicación demuestra que existe una dependencia transversal en el crecimiento del empleo regional a través de las regiones danesas. Asimismo, demostramos que esta dependencia puede ser explicada mediante los efectos fijos específicos de tiempo. Por lo tanto, las pruebas dejan al descubierto nuevas propiedades de los datos regionales.

Suggested Citation

  • Peter Sandholt Jensen & Torben Dall Schmidt, 2011. "Testing for Cross-sectional Dependence in Regional Panel Data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(4), pages 423-450, July.
  • Handle: RePEc:taf:specan:v:6:y:2011:i:4:p:423-450
    DOI: 10.1080/17421772.2011.610813
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    File URL: http://hdl.handle.net/10.1080/17421772.2011.610813
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    References listed on IDEAS

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    1. Pesaran, M.H., 2004. "‘General Diagnostic Tests for Cross Section Dependence in Panels’," Cambridge Working Papers in Economics 0435, Faculty of Economics, University of Cambridge.
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    Cited by:

    1. Torben Dall Schmidt & Aki Kangasharju & Timo Mitze & Daniel Rauhut, 2014. "The impact of aging on regional employment: Linking spatial econometrics and population projections for a scenario analysis of future labor market outcomes in Nordic regions," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 232-246.
    2. Torben Schmidt & Peter Jensen, 2013. "Foreign labor and regional labor markets: aggregate and disaggregate impact on growth and wages in Danish regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 50(3), pages 809-840, June.
    3. Alexander Chudik & M. Hashem Pesaran, 2013. "Large Panel Data Models with Cross-Sectional Dependence: A Survey," CESifo Working Paper Series 4371, CESifo Group Munich.
    4. B. Fingleton & P. Cheshire & H. Garretsen & D. Igliori & J. Le Gallo & P. McCann & J. McCombie & V. Monastiriotis & B. Moore & M. Roberts, 2011. "Editorial," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(4), pages 351-357, December.

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