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Robust estimation of personal income distribution models

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  • Victoria-Feser, Maria-Pia

Abstract

Statistical problems in modelling personal income distributions include estimation procedures, testing and model choice. Typically, the parameters of a given model are estimated by classical procedures such as maximum likelihood and least squares estimators. Unfortunately, the classical methods are very sensitive to model derivations such as gross errors in the data, grouping effects or model misspecifications. These deviations can ruin the values of the estimators and inequality measures and can produce false information about the distribution of the personal income in a given country. In this paper we discuss the use of robust techniques for the estimation of income distributions. These methods behave as the classical procedures at the model but are less influenced by model deviations and can be applied to general estimation problems.

Suggested Citation

  • Victoria-Feser, Maria-Pia, 1993. "Robust estimation of personal income distribution models," LSE Research Online Documents on Economics 6633, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:6633
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    File URL: http://eprints.lse.ac.uk/6633/
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    Cited by:

    1. Dorothée Boccanfuso & Bernard Decaluwé & Luc Savard, 2008. "Poverty, income distribution and CGE micro-simulation modeling: Does the functional form of distribution matter?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(2), pages 149-184, June.

    More about this item

    Keywords

    Personal income distribution; inequality measures; parametric models; influence function; M-estimator.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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