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Cluster analysis of panel data sets using non-standard optimisation of information criteria

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  • Kapetanios, George

Abstract

Panel datasets have been increasingly used in economics to analyse complex economic phenomena. One of the attractions of panel datasets is the ability to use an extended dataset to obtain information about parameters of interest which are assumed to have common values across panel units. However, the assumption of poolability has not been studied extensively beyond tests that determine whether a given dataset is poolable. We propose an information criterion method that enables the distinction of a set of series into a set of poolable series for which the hypothesis of a common parameter subvector cannot be reject and a set of series for which the poolability hypothesis fails. The method can be extended to analyse datasets with multiple clusters of series with similar characteristics. We discuss the theoretical properties of the method and investigate its small sample performance in a Monte Carlo study.
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  • Kapetanios, George, 2006. "Cluster analysis of panel data sets using non-standard optimisation of information criteria," Journal of Economic Dynamics and Control, Elsevier, vol. 30(8), pages 1389-1408, August.
    • Handle: RePEc:eee:dyncon:v:30:y:2006:i:8:p:1389-1408
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    Cited by:

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    2. Bottasso, Anna & Conti, Maurizio & Piacenz, Massimiliano & Vannoni, Davide, 2011. "The appropriateness of the poolability assumption for multiproduct technologies: Evidence from the English water and sewerage utilities," International Journal of Production Economics, Elsevier, vol. 130(1), pages 112-117, March.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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