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Iterative Kernel Density Estimation Applied to Grouped Data : Estimating Poverty and Inequality Indicators from the German Microcensus
Walter, Paul; Groß, Marcus; Schmid, Timo; u. a. (2022): „Iterative Kernel Density Estimation Applied to Grouped Data : Estimating Poverty and Inequality Indicators from the German Microcensus“. Bamberg: Otto-Friedrich-Universität.
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Year of publication:
2022
Pages:
Source/Other editions:
Journal of Official Statistics : JOS, 38 (2022), 2, S. 599-635 - ISSN: 2001-7367
Year of first publication:
2022
Language:
English
Abstract:
The estimation of poverty and inequality indicators based on survey data is trivial as long as the variable of interest (e.g., income or consumption) is measured on a metric scale. However, estimation is not directly possible, using standard formulas, when the income variable is grouped due to confidentiality constraints or in order to decrease item nonresponse. We propose an iterative kernel density algorithm that generates metric pseudo samples from the grouped variable for the estimation of indicators. The corresponding standard errors are estimated by a non-parametric bootstrap that accounts for the additional uncertainty due to the grouping. The algorithm enables the use of survey weights and household equivalence scales. The proposed method is applied to the German Microcensus for estimating the regional distribution of poverty and inequality in Germany.
GND Keywords: ; ; ;
Mikrozensus
Armut
Ungleichheit
Schätzung
Keywords: ; ; ; ;
Direct estimation
Interval-censored data
non-parametric estimation
OECD scale
prediction
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Type:
Article
published:
October 20, 2022
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https://fis.uni-bamberg.de/handle/uniba/55983