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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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  • George Kapetanios
  • M. Hashem Pesaran

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 CESifo Group Munich in its series CESifo Working Paper Series with number 1416.

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Date of creation: 2005
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Handle: RePEc:ces:ceswps:_1416

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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. Hanson, Samuel G. & Pesaran, M. Hashem & Schuermann, Til, 2008. "Firm heterogeneity and credit risk diversification," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 583-612, September.
  2. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  3. Pesaran, M.H. & Weiner, S.M., 2001. "Modelling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Cambridge Working Papers in Economics 0119, Faculty of Economics, University of Cambridge.
  4. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-304, September.
  5. 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 B5-3, International Conferences on Panel Data.
  6. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
  7. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, 07.
  8. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  9. 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.
  10. Lee, Kevin C & Pesaran, M Hashem, 1993. "The Role of Sectoral Interactions in Wage Determination in the UK Economy," Economic Journal, Royal Economic Society, vol. 103(416), pages 21-55, January.
  11. Timothy G. Conley & Bill Dupor, 2003. "A Spatial Analysis of Sectoral Complementarity," Journal of Political Economy, University of Chicago Press, vol. 111(2), pages 311-352, April.
  12. 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 0529, Faculty of Economics, University of Cambridge.
  13. Mario Forni & Lucrezia Reichlin, 1998. "Let's get real: a factor analytical approach to disaggregated business cycle dynamics," ULB Institutional Repository 2013/10147, ULB -- Universite Libre de Bruxelles.
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Cited by:
  1. M. Hashem Pesaran & Til Schuermann & Björn-Jakob Treutler, 2005. "Global Business Cycles and Credit Risk," NBER Working Papers 11493, National Bureau of Economic Research, Inc.
  2. Westerlund, Joakim & Urbain, Jean-Pierre, 2013. "On the estimation and inference in factor-augmented panel regressions with correlated loadings," Economics Letters, Elsevier, vol. 119(3), pages 247-250.
  3. Dees, S. & di Mauro, F. & Pesaran, M.H. & Smith, L.V., 2005. "Exploring the International Linkages of the Euro Area: a Global VAR Analysis," Cambridge Working Papers in Economics 0518, Faculty of Economics, University of Cambridge.
  4. Westerlund, Joakim & Urbain, Jean-Pierre, 2013. "On the implementation and use of factor-augmented regressions in panel data," Journal of Asian Economics, Elsevier, vol. 28(C), pages 3-11.
  5. Westerlund, Joakim & Reese, Simon, 2014. "Estimation of Factor-Augmented Panel Regressions with Weakly Influential Factors," Working Papers 2014:8, Lund University, Department of Economics.
  6. Su, Liangjun & Jin, Sainan, 2012. "Sieve estimation of panel data models with cross section dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 34-47.
  7. M. Hashem Pesaran & Ron Smith, 2006. "Macroeconometric Modelling With A Global Perspective," Manchester School, University of Manchester, vol. 74(s1), pages 24-49, 09.
  8. 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.

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