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A Principal Components Approach to Cross-Section Dependence in Panels

  • Jerry Coakley

    (Department of Accounting, Finance and Management, University of Essex)

  • Ana-Maria Fuertes

    (Faculty of Finance, City University Business School)

  • Ron Smith

    ()

    (Department of Economics, Birkbeck College, University of London)

The use of GLS to deal with cross-section dependence in panels is not feasible where N is large relative to T since the disturbance covariance matrix is rank deficient. Neither is it the appropriate response if the dependence results from omitted global variables or common shocks correlated with the included regressors. These can be proxied by the principal components of the residuals from a baseline regression. It is shown that the OLS estimates from a regression augmented by these principal components are unbiased and consistent using sequential limits for large T, large N. Simulations show that this leads to a substantial reduction in bias even for relatively small T and N panels. An empirical application indicates that the impact of cross section dependence seems to strengthen the case for long run PPP.

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Paper provided by International Conferences on Panel Data in its series 10th International Conference on Panel Data, Berlin, July 5-6, 2002 with number B5-3.

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Date of creation: Mar 2002
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Handle: RePEc:cpd:pd2002:b5-3
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  1. 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.
  2. Anindya Banerjee & Massimiliano Marcellino & Chiara Osbat, 2005. "Testing for PPP: Should we use panel methods?," Empirical Economics, Springer, vol. 30(1), pages 77-91, January.
  3. Hyungsik Roger MOON & Benoit PERRON, 2002. "Testing For A Unit Root In Panels With Dynamic Factors," Cahiers de recherche 18-2002, Centre interuniversitaire de recherche en ├ęconomie quantitative, CIREQ.
  4. O'Connell, Paul G. J., 1998. "The overvaluation of purchasing power parity," Journal of International Economics, Elsevier, vol. 44(1), pages 1-19, February.
  5. Badi H. Baltagi & Chihwa Kao, 2000. "Nonstationary Panels, Cointegration in Panels and Dynamic Panels: A Survey," Center for Policy Research Working Papers 16, Center for Policy Research, Maxwell School, Syracuse University.
  6. Peter C.B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Cowles Foundation Discussion Papers 1222, Cowles Foundation for Research in Economics, Yale University.
  7. Pesaran, M.H. & Smith, R., 1992. "Estimating Long-Run Relationships From Dynamic Heterogeneous Panels," Cambridge Working Papers in Economics 9215, Faculty of Economics, University of Cambridge.
  8. Peter C.B. Phillips & Hyungsik R. Moon, 1999. "Nonstationary Panel Data Analysis: An Overview of Some Recent Developments," Cowles Foundation Discussion Papers 1221, Cowles Foundation for Research in Economics, Yale University.
  9. Ron Smith & Ana-Maria Fuertes & Jerry Coakley, 2001. "Small Sample Properties of Panel Time-series Estimators with I(1) Errors," Working Papers wp01-08, Warwick Business School, Finance Group.
  10. Jushan Bai & Serena Ng, 2001. "A New Look at Panel Testing of Stationarity and the PPP Hypothesis," Economics Working Paper Archive 467, The Johns Hopkins University,Department of Economics.
  11. Hall, Stephen & Lazarova, Stepana & Urga, Giovanni, 1999. " A Principal Components Analysis of Common Stochastic Trends in Heterogeneous Panel Data: Some Monte Carlo Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 749-67, Special I.
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