IDEAS home Printed from https://ideas.repec.org/a/bla/jecsur/v23y2009i3p528-561.html
   My bibliography  Save this article

A Review And Comparison Of Tests Of Cross‐Section Independence In Panels

Author

Listed:
  • Francesco Moscone
  • Elisa Tosetti

Abstract

In this paper we review and compare diagnostic tests of cross‐section independence in the disturbances of panel regression models. We examine tests based on the sample pairwise correlation coefficient or on its transformations, and tests based on the theory of spacings. The ultimate goal is to shed some light on the appropriate use of existing diagnostic tests for cross‐equation error correlation. Our discussion is supported by means of a set of Monte Carlo experiments and a small empirical study on health. Results show that tests based on the average of pairwise correlation coefficients work well when the alternative hypothesis is a factor model with non‐zero mean loadings. Tests based on spacings are powerful in identifying various forms of strong cross‐section dependence, but have low power when they are used to capture spatial correlation.

Suggested Citation

  • Francesco Moscone & Elisa Tosetti, 2009. "A Review And Comparison Of Tests Of Cross‐Section Independence In Panels," Journal of Economic Surveys, Wiley Blackwell, vol. 23(3), pages 528-561, July.
    • Handle: RePEc:bla:jecsur:v:23:y:2009:i:3:p:528-561
    DOI: 10.1111/j.1467-6419.2008.00571.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-6419.2008.00571.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-6419.2008.00571.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dufour, Jean-Marie & Khalaf, Lynda, 2002. "Exact tests for contemporaneous correlation of disturbances in seemingly unrelated regressions," Journal of Econometrics, Elsevier, vol. 106(1), pages 143-170, January.
    2. George Kapetanios, 2004. "A New Method for Determining the Number of Factors in Factor Models with Large Datasets," Working Papers 525, Queen Mary University of London, School of Economics and Finance.
    3. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    4. George Kapetanios, 2004. "A New Method for Determining the Number of Factors in Factor Models with Large Datasets," Working Papers 525, Queen Mary University of London, School of Economics and Finance.
    5. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    6. Swamy, P A V B, 1970. "Efficient Inference in a Random Coefficient Regression Model," Econometrica, Econometric Society, vol. 38(2), pages 311-323, March.
    7. Donald W. K. Andrews, 2005. "Cross-Section Regression with Common Shocks," Econometrica, Econometric Society, vol. 73(5), pages 1551-1585, September.
    8. Chudik, Alexander & Pesaran, M. Hashem, 2011. "Infinite-dimensional VARs and factor models," Journal of Econometrics, Elsevier, vol. 163(1), pages 4-22, July.
    9. Pesaran, M. Hashem & Tosetti, Elisa, 2011. "Large panels with common factors and spatial correlation," Journal of Econometrics, Elsevier, vol. 161(2), pages 182-202, April.
    10. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    11. M. Hashem Pesaran & Aman Ullah & Takashi Yamagata, 2008. "A bias-adjusted LM test of error cross-section independence," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 105-127, March.
    12. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    13. Amengual, Dante & Watson, Mark W., 2007. "Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 91-96, January.
    14. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
    15. Frees, Edward W., 1995. "Assessing cross-sectional correlation in panel data," Journal of Econometrics, Elsevier, vol. 69(2), pages 393-414, October.
    16. Maddala, G S & Wu, Shaowen, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 631-652, Special I.
    17. Jammalamadaka, S.R.S. Rao & Goria, M. N., 2004. "A test of goodness-of-fit based on Gini's index of spacings," Statistics & Probability Letters, Elsevier, vol. 68(2), pages 177-187, June.
    18. Peter C. B. Phillips & Donggyu Sul, 2003. "Dynamic panel estimation and homogeneity testing under cross section dependence *," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 217-259, June.
    19. Tsay, Wen-Jen, 2004. "Testing for contemporaneous correlation of disturbances in seemingly unrelated regressions with serial dependence," Economics Letters, Elsevier, vol. 83(1), pages 69-76, April.
    20. Cooper, Russell & Haltiwanger, John, 1996. "Evidence on Macroeconomic Complementarities," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 78-93, February.
    21. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    22. Ng, Serena, 2006. "Testing Cross-Section Correlation in Panel Data Using Spacings," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 12-23, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexander Chudik & M. Hashem Pesaran, 2013. "Large panel data models with cross-sectional dependence: a survey," Globalization Institute Working Papers 153, Federal Reserve Bank of Dallas.
    2. Moscone, F. & Tosetti, E., 2010. "Testing for error cross section independence with an application to US health expenditure," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 283-291, September.
    3. Schneider, Nicolas & Strielkowski, Wadim, 2023. "Modelling the unit root properties of electricity data—A general note on time-domain applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    4. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    5. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.
    6. Mariam Camarero & Sergi Moliner & Cecilio Tamarit, 2022. "Which are the long-run determinants of US outward FDI? Evidence using large long-memory panels," Working Papers 2022.08, International Network for Economic Research - INFER.
    7. Ozcan, Burcu, 2013. "The nexus between carbon emissions, energy consumption and economic growth in Middle East countries: A panel data analysis," Energy Policy, Elsevier, vol. 62(C), pages 1138-1147.
    8. Hamit-Haggar, Mahamat, 2012. "Greenhouse gas emissions, energy consumption and economic growth: A panel cointegration analysis from Canadian industrial sector perspective," Energy Economics, Elsevier, vol. 34(1), pages 358-364.
    9. Hsiao, Cheng & Pesaran, M. Hashem & Pick, Andreas, 2007. "Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models," IZA Discussion Papers 2756, Institute of Labor Economics (IZA).
    10. Sarafidis, Vasilis & Yamagata, Takashi & Robertson, Donald, 2009. "A test of cross section dependence for a linear dynamic panel model with regressors," Journal of Econometrics, Elsevier, vol. 148(2), pages 149-161, February.
    11. Markus Eberhardt & Francis Teal, 2008. "Modeling Technology and Technological Change in Manufacturing: How do Countries Differ?," CSAE Working Paper Series 2008-12, Centre for the Study of African Economies, University of Oxford.
    12. Christoph Strumann, 2019. "Hodges–Lehmann Estimation of Static Panel Models with Spatially Correlated Disturbances," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 141-168, January.
    13. Akgun, Oguzhan & Pirotte, Alain & Urga, Giovanni, 2020. "Forecasting using heterogeneous panels with cross-sectional dependence," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1211-1227.
    14. Tiba, Sofien & Belaid, Fateh, 2020. "The pollution concern in the era of globalization: Do the contribution of foreign direct investment and trade openness matter?," Energy Economics, Elsevier, vol. 92(C).
    15. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2012. "A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model," Journal of Econometrics, Elsevier, vol. 170(1), pages 164-177.
    16. Declan French, 2012. "Causation between health and income: a need to panic," Empirical Economics, Springer, vol. 42(2), pages 583-601, April.
    17. Xie, Zixiong & Chen, Shyh-Wei, 2019. "Exchange rates and fundamentals: A bootstrap panel data analysis," Economic Modelling, Elsevier, vol. 78(C), pages 209-224.
    18. Badi H. Baltagi & Qu Feng & Chihwa Kao, 2009. "Testing for Sphericity in a Fixed Effects Panel Data Model (Revised July 2009)," Center for Policy Research Working Papers 112, Center for Policy Research, Maxwell School, Syracuse University.
    19. R. Golinelli & I. Mammi & A. Musolesi, 2018. "Parameter heterogeneity, persistence and cross-sectional dependence: new insights on fiscal policy reaction functions for the Euro area," Working Papers wp1120, Dipartimento Scienze Economiche, Universita' di Bologna.
    20. Shyh-Wei Chen & Zixiong Xie & Ying Liao, 2018. "Energy consumption promotes economic growth or economic growth causes energy use in China? A panel data analysis," Empirical Economics, Springer, vol. 55(3), pages 1019-1043, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jecsur:v:23:y:2009:i:3:p:528-561. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0950-0804 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.