TY  - JOUR
AU  - Manning,Willard G.
AU  - Basu,Anirban
AU  - Mullahy,John
TI  - Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data
JF  - National Bureau of Economic Research Technical Working Paper Series
VL  - No. 293
PY  - 2003
Y2  - October 2003
UR  - http://www.nber.org/papers/t0293
L1  - http://www.nber.org/papers/t0293.pdf
N1  - Author contact info:
John Mullahy
University of Wisconsin-Madison
Dept. of Population Health Sciences
787 WARF, 610 N. Walnut Street
Madison, WI 53726
Tel: 608/265-5410
Fax: 608/263-2820
E-Mail: jmullahy@facstaff.wisc.edu
AB  - 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.
ER  -