An alternative method to estimate income variance in cross-sectional data

Hiroki Uematsu; Ashok Kumar Mishra; Rebekah Rachel Powell

doi:10.1080/13504851.2011.631887

An alternative method to estimate income variance in cross-sectional data

Hiroki Uematsu, Ashok Kumar Mishra, Rebekah Rachel Powell

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

A popular approach to estimating income variance in cross-sectional data is to use an aggregate method by categorizing sample observations into arbitrarily formed groups, taking into account some socio-economic attributes. This study proposes an alternative technique that can be used to estimate income variance from cross-sectional data. Results indicate that this multiplicative heteroskedastic feasible least squares estimation procedure is consistent and efficient, consumes less time and requires less manipulation of data.

Original language	English (US)
Pages (from-to)	1431-1436
Number of pages	6
Journal	Applied Economics Letters
Volume	19
Issue number	15
DOIs	https://doi.org/10.1080/13504851.2011.631887
State	Published - Oct 2012
Externally published	Yes

Keywords

aggregate approach
cross-sectional data
feasible generalized least squares
income variance
multiplicative heteroskedasticity

ASJC Scopus subject areas

Economics and Econometrics

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10.1080/13504851.2011.631887

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AU - Powell, Rebekah Rachel

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AB - A popular approach to estimating income variance in cross-sectional data is to use an aggregate method by categorizing sample observations into arbitrarily formed groups, taking into account some socio-economic attributes. This study proposes an alternative technique that can be used to estimate income variance from cross-sectional data. Results indicate that this multiplicative heteroskedastic feasible least squares estimation procedure is consistent and efficient, consumes less time and requires less manipulation of data.

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KW - feasible generalized least squares

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KW - multiplicative heteroskedasticity

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An alternative method to estimate income variance in cross-sectional data

Abstract

Keywords

ASJC Scopus subject areas

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