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Statistical Methods for Distributional Analysis

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This Chapter is about the techniques, formal and informal, that are commonly used to give quantitative answers in the field of distributional analysis - covering subjects including inequality, poverty and the modelling of income distributions. It deals with parametric and non-parametric approaches and the way in which imperfections in data may be handled in practice.

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  • Franck A. Cowell & Emmanuel Flachaire, 2015. "Statistical Methods for Distributional Analysis," AMSE Working Papers 1507, Aix-Marseille School of Economics, Marseille, France.
  • Handle: RePEc:aim:wpaimx:1507
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    Cited by:

    1. Engelhardt, Carina & Wagener, Andreas, 2017. "The income distribution of voters: a case study from Germany," Hannover Economic Papers (HEP) dp-586, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Oancea, Bogdan & Andrei, Tudorel & Pirjol, Dan, 2017. "Income inequality in Romania: The exponential-Pareto distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 486-498.

    More about this item

    Keywords

    goodness of fit; parametric modelling; non-parametric methods; dominance criteria; welfare indices; inequality measure; poverty measure; influence function; hypothesis testing; confidence intervals; bootstrap;

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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