Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data

Willard Manning, Anirban Basu and John Mullahy ()

No 293, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: There are two broad classes of models used to address the econometric problems caused by skewness in data commonly encountered in health care applications: (1) transformation to deal with skewness (e.g., OLS on ln(y)); and (2) alternative weighting approaches based on exponential conditional models (ECM) and generalized linear model (GLM) approaches. In this paper, we encompass these two classes of models using the three parameter generalized gamma (GGM) distribution, which includes several of the standard alternatives as special cases OLS with a normal error, OLS for the log normal, the standard gamma and exponential with a log link, and the Weibull. Using simulation methods, we find the tests of identifying distributions to be robust. The GGM also provides a potentially more robust alternative estimator to the standard alternatives. An example using inpatient expenditures is also analyzed.

JEL-codes: I1 (search for similar items in EconPapers)
Date: 2003-10
New Economics Papers: this item is included in nep-edu
Note: TWP
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Citations: View citations in EconPapers (5)

Published as Manning, Willard G., Anirban Basu and John Mullahy. "Generalized Modeling Approaches To Risk Adjustment Of Skewed Outcomes Data," Journal of Health Economics, 2005, v24(3,May), 465-488.

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Journal Article: Generalized modeling approaches to risk adjustment of skewed outcomes data (2005)
Working Paper: Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data (2003)
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