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    Efficiency measurement. A methodological comparison of parametric and non-parametric approaches.

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    REVISED PHD THESIS W ZHENG.pdf (2.116Mb)
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    Publication date
    2015-06-24
    Author
    Zheng, Wanyu
    Supervisor
    Wallace, James
    Ward, Damian
    Keyword
    Technical efficiency, Data envelopment analysis, Stochastic frontier analysis, Distribution free approach, Simulated data, Insurance industry
    Rights
    Creative Commons License
    The University of Bradford theses are licenced under a Creative Commons Licence.
    Institution
    University of Bradford
    Department
    School of Management
    Awarded
    2013
    
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    Abstract
    The thesis examines technical efficiency using frontier efficiency estimation techniques from parametric and non-parametric approaches. Five different frontier efficiency estimation techniques are considered which are SFA, DFA, DEA-CCR, DEA-BCC and DEA-RAM. These techniques are then used on an artificially generated panel dataset using a two-input two-output production function framework based on characteristics of German life-insurers. The key contribution of the thesis is firstly, a study that uses simulated panel dataset to estimate frontier efficiency techniques and secondly, a research framework that compares multiple frontier efficiency techniques across parametric and non-parametric approaches in the context of simulated panel data. The findings suggest that, as opposed to previous studies, parametric and non-parametric approaches can both generate comparable technical efficiency scores with simulated data. Moreover, techniques from parametric approaches, i.e. SFA and DFA are consistent with each other whereas the same applies to non-parametric approaches, i.e. DEA models. The research study also discusses some important theoretical and methodological implication of the findings and suggests some ways whereby future research can enable to overcome some of the restrictions associated with current approaches.
    URI
    http://hdl.handle.net/10454/7294
    Type
    Thesis
    Qualification name
    PhD
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    Theses

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