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Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling of Asset Returns

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  • Kapetanios, G.
  • Pesaran, M.H.

Abstract

This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares two alternative methods for carrying out estimation and inference in panels with a multifactor error structure. One uses the correlated common effects estimator that proxies the unobserved factors by cross section averages of the observed variables as suggested by Pesaran (2004) , and the other uses principal components following the work of Stock and Watson (2002) . The paper develops the principal component method and provides small sample evidence on the comparative properties of these estimators by means of extensive Monte Carlo experiments. An empirical application to company returns provides an illustration of the alternative estimation procedures.

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Bibliographic Info

Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0520.

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Length: 37
Date of creation: May 2005
Date of revision:
Handle: RePEc:cam:camdae:0520

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Keywords: Cross Section Dependence; Large Panels; Principal Components; Common Correlated Effects; Return Equations.;

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References

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  1. Jerry Coakley & Ana-Maria Fuertes & Ron Smith, 2002. "A Principal Components Approach to Cross-Section Dependence in Panels," 10th International Conference on Panel Data, Berlin, July 5-6, 2002, International Conferences on Panel Data B5-3, International Conferences on Panel Data.
  2. M. Hashem Pesaran & Til Schuermann & Scott M. Weiner, 2002. "Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Center for Financial Institutions Working Papers, Wharton School Center for Financial Institutions, University of Pennsylvania 01-38, Wharton School Center for Financial Institutions, University of Pennsylvania.
  3. Forni, Mario & Reichlin, Lucrezia, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 65(3), pages 453-73, July.
  4. Lee, K.C. & Pesaran, M.H., 1992. "The Role of Sectoral Interactions in Wage Determination in the UK Economy," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 9214, Faculty of Economics, University of Cambridge.
  5. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers, C.E.P.R. Discussion Papers 2338, C.E.P.R. Discussion Papers.
  6. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, Elsevier, vol. 15(3), pages 373-394, March.
  7. Chamberlain, Gary & Rothschild, Michael, 1982. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Scholarly Articles 3230355, Harvard University Department of Economics.
  8. Timothy G. Conley & Bill Dupor, 2003. "A Spatial Analysis of Sectoral Complementarity," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 111(2), pages 311-352, April.
  9. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, Elsevier, vol. 68(1), pages 79-113, July.
  10. Samuel Hanson & M. Hashem Pesaran & Til Schuermann, 2005. "Firm Heterogeneity and Credit Risk Diversification," CESifo Working Paper Series 1531, CESifo Group Munich.
  11. Pesaran, M.H. & Schuermann, T. & Treutler, B-J., 2005. "The Role of Industry, Geography and Firm Heterogeneity in Credit Risk Diversification," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 0529, Faculty of Economics, University of Cambridge.
  12. M. Hashem Pesaran, 2004. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," CESifo Working Paper Series 1331, CESifo Group Munich.
  13. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(2), pages 147-62, April.
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Cited by:
  1. Stephane Dees & Filippo di Mauro & M. Hashem Pesaran & L. Vanessa Smith, 2005. "Exploring the International Linkages of the Euro Area: a Global VAR Analysis," CESifo Working Paper Series 1425, CESifo Group Munich.
  2. M. Hashem Pesaran & Til Schuermann & Bjorn-Jakob Treutler, 2007. "Global Business Cycles and Credit Risk," NBER Chapters, in: The Risks of Financial Institutions, pages 419-474 National Bureau of Economic Research, Inc.
  3. M. Hashem Pesaran & Ron Smith, 2006. "Macroeconometric Modelling With A Global Perspective," Manchester School, University of Manchester, vol. 74(s1), pages 24-49, 09.
  4. Castagnetti, Carolina & Rossi, Eduardo, 2008. "Estimation methods in panel data models with observed and unobserved components: a Monte Carlo study," MPRA Paper 26196, University Library of Munich, Germany.
  5. Su, Liangjun & Jin, Sainan, 2012. "Sieve estimation of panel data models with cross section dependence," Journal of Econometrics, Elsevier, Elsevier, vol. 169(1), pages 34-47.
  6. Westerlund, Joakim & Urbain, Jean-Pierre, 2013. "On the implementation and use of factor-augmented regressions in panel data," Journal of Asian Economics, Elsevier, Elsevier, vol. 28(C), pages 3-11.
  7. Westerlund, Joakim & Urbain, Jean-Pierre, 2013. "On the estimation and inference in factor-augmented panel regressions with correlated loadings," Economics Letters, Elsevier, Elsevier, vol. 119(3), pages 247-250.
  8. Westerlund, Joakim & Reese, Simon, 2014. "Estimation of Factor-Augmented Panel Regressions with Weakly Influential Factors," Working Papers, Lund University, Department of Economics 2014:8, Lund University, Department of Economics.

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