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To Pool or Not to Pool: A Partially Heterogeneous Framework

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  • Sarafidis, Vasilis
  • Weber, Neville

Abstract

This paper proposes a partially heterogeneous framework for the analysis of panel data with fixed T , based on the concept of "partitional clustering". In particular, the population of cross-sectional units is grouped into clusters, such that parameter homogeneity is maintained only within clusters. To de- termine the (unknown) number of clusters we propose an information-based criterion, which, as we show, is strongly consistent - i.e. it selects the true number of clusters with probability one as N approaches infinity. Simulation experiments show that the proposed criterion performs well even with moderate N and the resulting parameter estimates are close to the true values. We apply the method in a panel data set of commercial banks in the US and we find four clusters, with significant differences in the slope parameters across clusters.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 20814.

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Date of creation: 08 Dec 2009
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Handle: RePEc:pra:mprapa:20814

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Keywords: Partial heterogeneity; partitional clustering; information-based criterion; model selection;

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  1. Galor, Oded, 1996. "Convergence? Inferences from Theoretical Models," CEPR Discussion Papers 1350, C.E.P.R. Discussion Papers.
  2. Kaparakis, Emmanuel I & Miller, Stephen M & Noulas, Athanasios G, 1994. "Short-Run Cost Inefficiency of Commercial Banks: A Flexible Stochastic Frontier Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 26(4), pages 875-93, November.
  3. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2002. "Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption," Economics Letters, Elsevier, vol. 76(3), pages 375-382, August.
  4. M. Hashem Pesaran, 2004. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," CESifo Working Paper Series 1331, CESifo Group Munich.
  5. Jonathan Temple, 1999. "The New Growth Evidence," Journal of Economic Literature, American Economic Association, vol. 37(1), pages 112-156, March.
  6. Durlauf, S.N. & Johnson, P.A., 1994. "Multiple Regimes and Cross-Country Growth Behavior," Working papers 9419, Wisconsin Madison - Social Systems.
  7. Baltagi, Badi H. & Griffin, James M., 1997. "Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline," Journal of Econometrics, Elsevier, vol. 77(2), pages 303-327, April.
  8. Allen N. Berger & David B. Humphrey, 1997. "Efficiency of Financial Institutions: International Survey and Directions for Future Research," Center for Financial Institutions Working Papers 97-05, Wharton School Center for Financial Institutions, University of Pennsylvania.
  9. Antweiler, Werner, 2001. "Nested random effects estimation in unbalanced panel data," Journal of Econometrics, Elsevier, vol. 101(2), pages 295-313, April.
  10. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
  11. 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.
  12. Badi H. Baltagi & James M. Griffin & Weiwen Xiong, 2000. "To Pool Or Not To Pool: Homogeneous Versus Hetergeneous Estimations Applied to Cigarette Demand," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 117-126, February.
  13. H. Baltagi, Badi & Heun Song, Seuck & Cheol Jung, Byoung, 2001. "The unbalanced nested error component regression model," Journal of Econometrics, Elsevier, vol. 101(2), pages 357-381, April.
  14. Burnside, Craig, 1996. "Production function regressions, returns to scale, and externalities," Journal of Monetary Economics, Elsevier, vol. 37(2-3), pages 177-201, April.
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