Individual health care expenditures have complex non-normal distributions with severe positive skewness and leptokurtosis. These features present severe challenges to reliable modeling of expenditures for prediction purposes. We compare a variety of methods using quasi-experimental techniques. Our quasi-experiments combine the distributional realism of actual data on health care expenditures with the reliability of Monte Carlo experimental results. We find that models based on Gamma densities predict substantially better than models based on linear regression with and without transformation of the dependent variable. Models based on finite mixtures of Gamma densities show further improvement in predictive properties.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
file. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)