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Grouped Patterns Of Heterogeneity In Panel Data

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  • Stéphane Bonhomme

    ()
    (CEMFI, Centro de Estudios Monetarios y Financieros)

  • Elena Manresa

    ()
    (CEMFI, Centro de Estudios Monetarios y Financieros)

Abstract

This paper introduces time-varying grouped patterns of heterogeneity in linear panel data models. A distinctive feature of our approach is that group membership is left unspecified. We estimate the model’s parameters using a “grouped fixed-effects” estimator that minimizes a least-squares criterion with respect to all possible groupings of the cross-sectional units. We rely on recent advances in the clustering literature for fast and efficient computation. Our estimator is higher-order unbiased as both dimensions of the panel tend to infinity, under conditions that we characterize. As a result, inference is not affected by the fact that group membership is estimated. We apply our approach to study the link between income and democracy across countries, while allowing for grouped patterns of unobserved heterogeneity. The results shed new light on the evolution of political and economic outcomes of countries.

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Bibliographic Info

Paper provided by CEMFI in its series Working Papers with number wp2012_1208.

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Date of creation: Jun 2012
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Handle: RePEc:cmf:wpaper:wp2012_1208

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Keywords: Discrete heterogeneity; panel data; fixed effects; democracy.;

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References

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  1. Antonio Ciccone, 2013. "Estimating the Effect of Transitory Economic Shocks on Civil Conflict," Review of Economics and Institutions, Università di Perugia, vol. 4(2).
  2. Markus Brückner & Antonio Ciccone, 2011. "Rain and the Democratic Window of Opportunity," Econometrica, Econometric Society, vol. 79(3), pages 923-947, 05.
  3. Martin Browning & Jesus M. Carro, 2009. "Dynamic binary outcome models with maximal heterogeneity," Economics Series Working Papers 426, University of Oxford, Department of Economics.
  4. Durlauf, Steven N. & Kourtellos, Andros & Minkin, Artur, 2001. "The local Solow growth model," European Economic Review, Elsevier, vol. 45(4-6), pages 928-940, May.
  5. Manuel Arellano & Stéphane Bonhomme, 2009. "Robust Priors in Nonlinear Panel Data Models," Econometrica, Econometric Society, vol. 77(2), pages 489-536, 03.
  6. Manuel Arellano & Stéphane Bonhomme, 2009. "Identifying Distributional Characteristics In Random Coefficients Panel Data Models," Working Papers wp2009_0904, CEMFI.
  7. Papaioannou, Elias & Siourounis, Gregorios, 2008. "Economic and Social Factors Driving the Third Wave of Democratization," CEPR Discussion Papers 6986, C.E.P.R. Discussion Papers.
  8. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
  9. Hahn, Jinyong & Moon, Hyungsik Roger, 2010. "Panel Data Models With Finite Number Of Multiple Equilibria," Econometric Theory, Cambridge University Press, vol. 26(03), pages 863-881, June.
  10. Martin Browning & Jesus Carro, 2006. "Heterogeneity and Microeconometrics Modelling," CAM Working Papers 2006-03, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
  11. Hyungsik Roger Moon & Martin Weidner, 2013. "Linear regression for panel with unknown number of factors as interactive fixed effects," CeMMAP working papers CWP49/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  12. Geweke, John & Keane, Michael, 2007. "Smoothly mixing regressions," Journal of Econometrics, Elsevier, vol. 138(1), pages 252-290, May.
  13. Hiroyuki Kasahara & Katsumi Shimotsu, 2009. "Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices," Econometrica, Econometric Society, vol. 77(1), pages 135-175, 01.
  14. Sam Schulhofer-Wohl, 2011. "Heterogeneity and Tests of Risk Sharing," Journal of Political Economy, University of Chicago Press, vol. 119(5), pages 925 - 958.
  15. Hansen, Christian B., 2007. "Asymptotic properties of a robust variance matrix estimator for panel data when T is large," Journal of Econometrics, Elsevier, vol. 141(2), pages 597-620, December.
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