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Statistical inference for inequality and poverty measures with dependent data

Statistical inference for inequality and poverty measures with dependent data
Statistical inference for inequality and poverty measures with dependent data
This article is about statistical inference for inequality and poverty measures when income data exhibit contemporaneous dependence across members of the same household. While much empirical research is based on household survey data such as the PSID, standard methods assume that income is an independent and identically distributed random variable. Applying them to contemporaneously dependent data produces biased results, and Monte Carlo experiments reveal that their confidence intervals are too narrow. By contrast, our proposed distribution-free estimators perform well.
0020-6598
185-200
Schluter, C.
ae043254-4cc4-48aa-abad-56a36554de2b
Trede, M.
5ad7e623-b129-4796-a5bb-b1e56cf2f9a6
Schluter, C.
ae043254-4cc4-48aa-abad-56a36554de2b
Trede, M.
5ad7e623-b129-4796-a5bb-b1e56cf2f9a6

Schluter, C. and Trede, M. (2002) Statistical inference for inequality and poverty measures with dependent data. International Economic Review, 43 (2), 185-200. (doi:10.1111/1468-2354.t01-1-00024).

Record type: Article

Abstract

This article is about statistical inference for inequality and poverty measures when income data exhibit contemporaneous dependence across members of the same household. While much empirical research is based on household survey data such as the PSID, standard methods assume that income is an independent and identically distributed random variable. Applying them to contemporaneously dependent data produces biased results, and Monte Carlo experiments reveal that their confidence intervals are too narrow. By contrast, our proposed distribution-free estimators perform well.

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Published date: May 2002

Identifiers

Local EPrints ID: 33356
URI: http://eprints.soton.ac.uk/id/eprint/33356
ISSN: 0020-6598
PURE UUID: ecc6ea68-a3f7-44f3-8e97-93e35481163e

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Date deposited: 17 May 2006
Last modified: 15 Mar 2024 07:43

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Contributors

Author: C. Schluter
Author: M. Trede

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