Sarafidis, Vasilis and Weber, Neville (2009): To Pool or Not to Pool: A Partially Heterogeneous Framework.
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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.
|Item Type:||MPRA Paper|
|Original Title:||To Pool or Not to Pool: A Partially Heterogeneous Framework|
|Keywords:||Partial heterogeneity, partitional clustering, information-based criterion, model selection|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C33 - Models with Panel Data; Longitudinal Data; Spatial Time Series
|Depositing User:||Vasilis Sarafidis|
|Date Deposited:||20. Feb 2010 16:57|
|Last Modified:||19. Feb 2013 01:23|
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