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Variance estimation of change of poverty based upon the Turkish EU-SILC survey

Variance estimation of change of poverty based upon the Turkish EU-SILC survey
Variance estimation of change of poverty based upon the Turkish EU-SILC survey
Interpreting differences between point estimates at different waves may be misleading, if we do not take the sampling variation into account. It is therefore necessary to estimate the standard error of these differences in order to judge whether or not the observed differences are statistically significant. A major problem is to take into account of temporal correlations between estimators. Correlations play an important role in estimating the variance of a change between cross-sectional estimates. The standard correlation can be biased, because of the rotation of the design used for the European Union Statistics on Income and Living Conditions (EU-SILC) surveys. Furthermore, poverty rates depend on poverty thresholds which are estimated. We propose to use a multivariate linear regression approach to estimate the correlations. We also show how this approach can be adjusted to account for the estimation of poverty thresholds. The proposed estimator is not a model-based estimator, as this estimator is valid even if the model does not fit the data. We implemented the proposed approach to the Turkish EUSILC survey data.
linearisation, multivariate regression, stratification, unequal inclusion probabilities
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Oguz Alper, Melike
a10e9024-9f7a-4e87-a114-dc6b0cc4daa1
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Oguz Alper, Melike
a10e9024-9f7a-4e87-a114-dc6b0cc4daa1

Berger, Yves G. and Oguz Alper, Melike (2013) Variance estimation of change of poverty based upon the Turkish EU-SILC survey. NTTS (New Techniques and Technologies for Statistics) 2013. 05 - 07 Mar 2013. 8 pp . (doi:10.2901/Eurostat.C2013.001).

Record type: Conference or Workshop Item (Paper)

Abstract

Interpreting differences between point estimates at different waves may be misleading, if we do not take the sampling variation into account. It is therefore necessary to estimate the standard error of these differences in order to judge whether or not the observed differences are statistically significant. A major problem is to take into account of temporal correlations between estimators. Correlations play an important role in estimating the variance of a change between cross-sectional estimates. The standard correlation can be biased, because of the rotation of the design used for the European Union Statistics on Income and Living Conditions (EU-SILC) surveys. Furthermore, poverty rates depend on poverty thresholds which are estimated. We propose to use a multivariate linear regression approach to estimate the correlations. We also show how this approach can be adjusted to account for the estimation of poverty thresholds. The proposed estimator is not a model-based estimator, as this estimator is valid even if the model does not fit the data. We implemented the proposed approach to the Turkish EUSILC survey data.

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More information

Published date: 5 March 2013
Venue - Dates: NTTS (New Techniques and Technologies for Statistics) 2013, 2013-03-05 - 2013-03-07
Related URLs:
Keywords: linearisation, multivariate regression, stratification, unequal inclusion probabilities
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 354894
URI: https://eprints.soton.ac.uk/id/eprint/354894
PURE UUID: 98c86fce-a56d-4db4-ab6b-b30607b119e3

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Date deposited: 07 Aug 2013 10:44
Last modified: 03 Jan 2018 17:35

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