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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.

Suggested Citation

  • Sarafidis, Vasilis & Weber, Neville, 2009. "To Pool or Not to Pool: A Partially Heterogeneous Framework," MPRA Paper 20814, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:20814
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    References listed on IDEAS

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    Cited by:

    1. Elena Stolyarova, 2013. "Carbon Dioxide Emissions, economic growth and energy mix: empirical evidence from 93 countries," EcoMod2013 5433, EcoMod.

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    More about this item

    Keywords

    Partial heterogeneity; partitional clustering; information-based criterion; model selection;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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