Modelling income distributions with limited data

Duangkamon Chotikapanich; William Griffiths; Gholamreza Hajargasht

doi:10.1007/978-981-19-2023-3_5

Modelling income distributions with limited data

Duangkamon Chotikapanich
, William Griffiths
, Gholamreza Hajargasht

Econometrics & Business Statistics

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review

Abstract

Minimum distance and maximum likelihood methods for estimating parametric income distributions from grouped data are summarized. Formulas for computing inequality and poverty measures from the parameters of the income distributions are presented. The paper is a convenient source for applied researchers wishing to estimate inequality and poverty from grouped data.

Original language	English
Title of host publication	Advances in Economic Measurement
Subtitle of host publication	A Volume in Honour of D. S. Prasada Rao
Editors	Duangkamon Chotikapanich, Alicia N. Rambaldi, Nicholas Rohde
Place of Publication	Singapore Singapore
Publisher	Springer
Pages	233-263
Number of pages	31
Edition	1st
ISBN (Electronic)	9789811920233
ISBN (Print)	9789811920226
DOIs	https://doi.org/10.1007/978-981-19-2023-3_5
Publication status	Published - 2022

Keywords

Grouped data
Lognormal distribution
Minimum distance estimators
Pareto-lognormal distribution

Access to Document

10.1007/978-981-19-2023-3_5

Cite this

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Modelling income distributions with limited data. / Chotikapanich, Duangkamon; Griffiths, William; Hajargasht, Gholamreza.
Advances in Economic Measurement: A Volume in Honour of D. S. Prasada Rao. ed. / Duangkamon Chotikapanich; Alicia N. Rambaldi; Nicholas Rohde. 1st. ed. Singapore Singapore: Springer, 2022. p. 233-263.

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review

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T1 - Modelling income distributions with limited data

AU - Chotikapanich, Duangkamon

AU - Griffiths, William

AU - Hajargasht, Gholamreza

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PY - 2022

Y1 - 2022

N2 - Minimum distance and maximum likelihood methods for estimating parametric income distributions from grouped data are summarized. Formulas for computing inequality and poverty measures from the parameters of the income distributions are presented. The paper is a convenient source for applied researchers wishing to estimate inequality and poverty from grouped data.

AB - Minimum distance and maximum likelihood methods for estimating parametric income distributions from grouped data are summarized. Formulas for computing inequality and poverty measures from the parameters of the income distributions are presented. The paper is a convenient source for applied researchers wishing to estimate inequality and poverty from grouped data.

KW - Grouped data

KW - Lognormal distribution

KW - Minimum distance estimators

KW - Pareto-lognormal distribution

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DO - 10.1007/978-981-19-2023-3_5

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AN - SCOPUS:85163546042

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EP - 263

BT - Advances in Economic Measurement

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A2 - Rambaldi, Alicia N.

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PB - Springer

CY - Singapore Singapore

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Modelling income distributions with limited data

Abstract

Keywords

Access to Document

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Research output

Advances in Economic Measurement: A Volume in Honour of D. S. Prasada Rao

Cite this