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. In particular, the population of cross-sectional units is grouped into clusters, such that slope parameter homogeneity is maintained only within clusters. Our method assumes no a priori information about the number of clusters and cluster membership and relies on the data instead. The unknown number of clusters and the corresponding partition are determined based on the concept of �partitional clustering�, using an information-based criterion. It is shown that this is strongly consistent, i.e. it selects the true number of clusters with probability one as N approaches unity. 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 five 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:||25. Jan 2012 02:39|
|Last Modified:||12. Feb 2013 10:26|
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To Pool or Not to Pool: A Partially Heterogeneous Framework. (deposited 20. Feb 2010 16:57)
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