Heterogeneity in ordered choice models: A review with applications to self-assessed health

Discrete variables that have an inherent sense of ordering across outcomes are commonly found in large datasets available to many economists, and are often the focus of research. However, assumptions underlying the standard Ordered Probit (which is usually used to analyse such variables) are not alw...

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Main Authors: Greene, W., Harris, Mark, Hollingsworth, B., Weterings, T.
Format: Working Paper
Published: Centre for Research in Applied Economics, Curtin Business School 2012
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/40029
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author Greene, W.
Harris, Mark
Hollingsworth, B.
Weterings, T.
author_facet Greene, W.
Harris, Mark
Hollingsworth, B.
Weterings, T.
author_sort Greene, W.
building Curtin Institutional Repository
collection Online Access
description Discrete variables that have an inherent sense of ordering across outcomes are commonly found in large datasets available to many economists, and are often the focus of research. However, assumptions underlying the standard Ordered Probit (which is usually used to analyse such variables) are not always justied by the data. This study provides a review of the ways in which the Ordered Probit might be extended to account for additional heterogeneity. Diering from other reviews in scope, application and relevance in economic settings, a series of issues pertaining to choices of variables, and the economic assumptions underlying each model are discussed in the context of measuring the underlying health of respondents. The models are applied to a wave of the Household, Income and Labour Dynamics in Australia survey, in order to check the appropriateness of such assumptions in an applied context.
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spelling curtin-20.500.11937-400292017-01-30T14:38:56Z Heterogeneity in ordered choice models: A review with applications to self-assessed health Greene, W. Harris, Mark Hollingsworth, B. Weterings, T. Self-Assessed Health HOPIT model Maximum Likelihood Ordered Choice Modelling Incorporating Heterogeneity Discrete variables that have an inherent sense of ordering across outcomes are commonly found in large datasets available to many economists, and are often the focus of research. However, assumptions underlying the standard Ordered Probit (which is usually used to analyse such variables) are not always justied by the data. This study provides a review of the ways in which the Ordered Probit might be extended to account for additional heterogeneity. Diering from other reviews in scope, application and relevance in economic settings, a series of issues pertaining to choices of variables, and the economic assumptions underlying each model are discussed in the context of measuring the underlying health of respondents. The models are applied to a wave of the Household, Income and Labour Dynamics in Australia survey, in order to check the appropriateness of such assumptions in an applied context. 2012 Working Paper http://hdl.handle.net/20.500.11937/40029 Centre for Research in Applied Economics, Curtin Business School fulltext
spellingShingle Self-Assessed Health
HOPIT model
Maximum Likelihood
Ordered Choice Modelling
Incorporating Heterogeneity
Greene, W.
Harris, Mark
Hollingsworth, B.
Weterings, T.
Heterogeneity in ordered choice models: A review with applications to self-assessed health
title Heterogeneity in ordered choice models: A review with applications to self-assessed health
title_full Heterogeneity in ordered choice models: A review with applications to self-assessed health
title_fullStr Heterogeneity in ordered choice models: A review with applications to self-assessed health
title_full_unstemmed Heterogeneity in ordered choice models: A review with applications to self-assessed health
title_short Heterogeneity in ordered choice models: A review with applications to self-assessed health
title_sort heterogeneity in ordered choice models: a review with applications to self-assessed health
topic Self-Assessed Health
HOPIT model
Maximum Likelihood
Ordered Choice Modelling
Incorporating Heterogeneity
url http://hdl.handle.net/20.500.11937/40029