Econometric risk adjustment, endogeneity, and extrapolation bias
By: John Mullahy
Contributors: Mullahy, John; National Bureau of Economic Research
Series: NBER working paper series -- working paper 12236; Working paper series (National Bureau of Economic Research : Online) -- working paper no. 12236.
Publisher: National Bureau of Economic Research
Published: 2006
Place of publication: Cambridge, MA
Language: eng
Format: [electronic resource] /
Description: "In econometric risk-adjustment exercises, models estimated with one or more included endogenous explanatory variables ("risk adjusters") will generally result in biased predictions of outcomes of interest, e.g.  unconditional mean healthcare expenditures.  This paper shows that a first-order contributor to this prediction bias is the difference between the distribution of explanatory variables in the estimation sample and the prediction sample -- a form of "extrapolation bias." In the linear model context, a difference in the means of the respective joint marginal distributions of observed covariates suffices to produce bias when endogenous explanatory variables are used in estimation.  If these means do not differ, then the "endogeneity-related" extrapolation bias disappears although a form of "standard" extrapolation bias may persist.  These results are extended to some of the nonlinear models in common use in this literature with some provisionally-similar conclusions.  In general the bias problem will be most acute where risk adjustment is most useful, i.e.  when estimated risk-adjustment models are applied in populations whose characteristics differ from those from which the estimation data are drawn"--National Bureau of Economic Research web site.
Notes: Title from PDF file as viewed on 5/25/2006.

Includes bibliographical references.

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