Domain prediction with grouped income data




Faculty/Professorship: Statistics and Econometrics  
Author(s): Walter, Paul; Groß, Marcus; Schmid, Timo ; Tzavidis, Nikos
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2022
Pages: 1501-1523
Source/Other editions: Journal of the Royal Statistical Society. Series A, Statistics in society, 184 (2021), 4, S. 1501-1523 - ISSN: 0964-1998
is version of: 10.1111/rssa.12736
Year of first publication: 2021
Language(s): English
Licence: Creative Commons - CC BY - Attribution 4.0 International 
URN: urn:nbn:de:bvb:473-irb-547742
Abstract: 
One popular small area estimation method for estimating poverty and inequality indicators is the empirical best predictor under the unit-level nested error regression model with a continuous dependent variable. However, parameter estimation is more challenging when the response variable is grouped due to data confidentiality concerns or concerns about survey response burden. The work in this paper proposes methodology that enables fitting a nested error regression model when the dependent variable is grouped. Model parameters are then used for small area prediction of finite population parameters of interest. Model fitting in the case of a grouped response variable is based on the use of a stochastic expectation–maximization algorithm. Since the stochastic expectation–maximization algorithm relies on the Gaussian assumptions of the unit-level error terms, adaptive transformations are incorporated for handling departures from normality. The estimation of the mean squared error of the small area parameters is facilitated by a parametric bootstrap that captures the additional uncertainty due to the grouping mechanism and the possible use of adaptive transformations. The empirical properties of the proposed methodology are assessed by using model-based simulations and its relevance is illustrated by estimating deprivation indicators for municipalities in the Mexican state of Chiapas.
GND Keywords: Einkommensstatistik; Intervallwahrscheinlichkeit; Regressionsanalyse; EM-Algorithmus; Methode der partiellen kleinsten Quadrate
Keywords: data confidentiality, interval-censored data, nested error regression model, small area estimation, survey response burden
DDC Classification: 330 Economics  
RVK Classification: QH 234   
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/54774
Release Date: 8. August 2022

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