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A Comparison of Alternative Models for the Demand for Medical Care

A Comparison of Alternative Models for the Demand for Medical Care We have tested alternative models of the demand for medical care using experimental data. The estimated response of demand to insurance plan is sensitive to the model used. We therefore use a split-sample analysis and find that a model that more closely approximates distributional assumptions and uses a nonparametric retransformation factor performs better in terms of mean squared forecast error. Simpler models are inferior either because they are not robust to outliers (e.g., ANOVA, ANOCOVA), or because they are inconsistent when strong distributional assumptions are violated (e.g., a two-parameter Box-Cox transformation). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Business & Economic Statistics Taylor & Francis

A Comparison of Alternative Models for the Demand for Medical Care

A Comparison of Alternative Models for the Demand for Medical Care

Journal of Business & Economic Statistics , Volume 1 (2): 12 – Apr 1, 1983

Abstract

We have tested alternative models of the demand for medical care using experimental data. The estimated response of demand to insurance plan is sensitive to the model used. We therefore use a split-sample analysis and find that a model that more closely approximates distributional assumptions and uses a nonparametric retransformation factor performs better in terms of mean squared forecast error. Simpler models are inferior either because they are not robust to outliers (e.g., ANOVA, ANOCOVA), or because they are inconsistent when strong distributional assumptions are violated (e.g., a two-parameter Box-Cox transformation).

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References (21)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1537-2707
eISSN
0735-0015
DOI
10.1080/07350015.1983.10509330
Publisher site
See Article on Publisher Site

Abstract

We have tested alternative models of the demand for medical care using experimental data. The estimated response of demand to insurance plan is sensitive to the model used. We therefore use a split-sample analysis and find that a model that more closely approximates distributional assumptions and uses a nonparametric retransformation factor performs better in terms of mean squared forecast error. Simpler models are inferior either because they are not robust to outliers (e.g., ANOVA, ANOCOVA), or because they are inconsistent when strong distributional assumptions are violated (e.g., a two-parameter Box-Cox transformation).

Journal

Journal of Business & Economic StatisticsTaylor & Francis

Published: Apr 1, 1983

Keywords: Health insurance; Cost sharing; Transformation; Forecast; Smearing estimate; Intrafamily correlation; Cross validation; Mean forecast squared error

There are no references for this article.