Robust income distribution estimation with missing data

Maria-Pia Victoria-Feser (maria.victoriafeser@unibo.it)

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: With income distributions it is common to encounter the problem of missing data. When a parametric model is fitted to the data, the problem can be overcome by specifying the marginal distribution of the observed data. With classical methods of estimation such as the maximum likelihood (ML) an estimator of the parameters can be obtained in a straightforward manner. Unfortunately, it is well known that ML estimators are not robust estimators in the presence of contaminated data. In this paper, we propose a robust alternative to the ML estimator with truncated data, namely one based on M-estimators that we call the EMM estimator. We present an extensive simulation study where the EMM estimator based on optimal B-robust estimators (OBRE) is compared to a more conservative approach based on marginal density (MD) for truncated data, and show that the difference lies in the way the weights associated to each observation are computed. Finally, we also compare the EMM estimator based on the OBRE with the classical ML estimator when the data are contaminated, and show that contrary to the former, the latter can be seriously biased.

Keywords: M-estimators; influence function; EM algorithm; truncated data. (search for similar items in EconPapers)
JEL-codes: C13 D31 D63 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2001-01
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http://eprints.lse.ac.uk/6561/ Open access version. (application/pdf)

Related works:
Working Paper: Robust Income Distribution Estimation with Missing Data (2001)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:6561

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