Artikel
Parametric inference for index functionals
In this paper, we study the finite sample accuracy of confidence intervals for index functional built via parametric bootstrap, in the case of inequality indices. To estimate the parameters of the assumed parametric data generating distribution, we propose a Generalized Method of Moment estimator that targets the quantity of interest, namely the considered inequality index. Its primary advantage is that the scale parameter does not need to be estimated to perform parametric bootstrap, since inequality measures are scale invariant. The very good finite sample coverages that are found in a simulation study suggest that this feature provides an advantage over the parametric bootstrap using the maximum likelihood estimator. We also find that overall, a parametric bootstrap provides more accurate inference than its non or semi-parametric counterparts, especially for heavy tailed income distributions.
- Sprache
-
Englisch
- Erschienen in
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 6 ; Year: 2018 ; Issue: 2 ; Pages: 1-11 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Econometric and Statistical Methods and Methodology: General
Estimation: General
Statistical Simulation Methods: General
Index Numbers and Aggregation; Leading indicators
Specific Distributions; Specific Statistics
Personal Income, Wealth, and Their Distributions
- Thema
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parametric bootstrap
generalized method of moments
income distribution
inequality measurement
heavy tail
- Ereignis
-
Geistige Schöpfung
- (wer)
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Guerrier, Stéphane
Orso, Samuel
Victoria-Feser, Maria-Pia
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2018
- DOI
-
doi:10.3390/econometrics6020022
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Guerrier, Stéphane
- Orso, Samuel
- Victoria-Feser, Maria-Pia
- MDPI
Entstanden
- 2018
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