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Large panel data models with cross-sectional dependence: a survey

Listed author(s):
  • Chudik, Alexander

    (Federal Reserve Bank of Dallas)

  • Pesaran, M. Hashem

This paper provides an overview of the recent literature on estimation and inference in large panel data models with cross-sectional dependence. It reviews panel data models with strictly exogenous regressors as well as dynamic models with weakly exogenous regressors. The paper begins with a review of the concepts of weak and strong cross-sectional dependence, and discusses the exponent of cross-sectional dependence that characterizes the different degrees of cross-sectional dependence. It considers a number of alternative estimators for static and dynamic panel data models, distinguishing between factor and spatial models of cross-sectional dependence. The paper also provides an overview of tests of independence and weak cross-sectional dependence.

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File URL: http://www.dallasfed.org/assets/documents/institute/wpapers/2013/0153.pdf
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Paper provided by Federal Reserve Bank of Dallas in its series Globalization and Monetary Policy Institute Working Paper with number 153.

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Length: 54 pages
Date of creation: 2013
Handle: RePEc:fip:feddgw:153
Note: Published as: Chudik, Alexander and M. Hashem Pesaran (2015), "Large Panel Data Models with Cross-Sectional Dependence: A Survey," in The Oxford Handbook of Panel Data, ed. Badi H. Baltagi (New York: Oxford University Press), 3-45.
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  1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004. "The generalized dynamic factor model consistency and rates," Journal of Econometrics, Elsevier, vol. 119(2), pages 231-255, April.
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  3. DHAENE, Geert & JOCHMANS, Koen, 2010. "Split-panel jackknife estimation of fixed-effect models," CORE Discussion Papers 2010003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. 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, 03.
  5. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
  6. Donald W. K. Andrews, 2005. "Cross-Section Regression with Common Shocks," Econometrica, Econometric Society, vol. 73(5), pages 1551-1585, 09.
  7. Kapetanios, George, 2010. "A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 397-409.
  8. Dufour, J.M. & Khalaf, L., 2000. "Exact Tests for Contemporaneous Correlation of Disturbances in Seemingly Unrelated Regressions," Cahiers de recherche 2000-11, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  9. Badi H. Baltagi & Qu Feng & Chihwa Kao, 2011. "Testing for sphericity in a fixed effects panel data model," Econometrics Journal, Royal Economic Society, vol. 14, pages 25-47, 02.
  10. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2009. "Weak and Strong Cross Section Dependence and Estimation of Large Panels," CESifo Working Paper Series 2689, CESifo Group Munich.
  11. In Choi, 2013. "Model Selection for Factor Analysis: Some New Criteria and Performance Comparisons," Working Papers 1209, Research Institute for Market Economy, Sogang University.
  12. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
  13. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
  14. Kapetanios, G. & Pesaran, M.H. & Yamagata, T., 2006. "Panels with Nonstationary Multifactor Error Structures," Cambridge Working Papers in Economics 0651, Faculty of Economics, University of Cambridge.
  15. Cheng Hsiao & M. Hashem Pesaran & Andreas Pick, 2012. "Diagnostic Tests of Cross‐section Independence for Limited Dependent Variable Panel Data Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 253-277, 04.
  16. 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.
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  19. M. Hashem Pesaran, 2004. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," CESifo Working Paper Series 1331, CESifo Group Munich.
  20. Nayoung Lee & Hyungsik Roger Moon & Martin Weidner, 2011. "Analysis of interactive fixed effects dynamic linear panel regression with measurement error," CeMMAP working papers CWP37/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  21. 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.
  22. T. De Groote & G. Everaert, 2011. "Common Correlated Effects Estimation of Dynamic Panels with Cross-Sectional Dependence," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/723, Ghent University, Faculty of Economics and Business Administration.
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  26. Pesaran, M.H. & Yamagata. T., 2005. "Testing Slope Homogeneity in Large Panels," Cambridge Working Papers in Economics 0513, Faculty of Economics, University of Cambridge.
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  40. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
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