A flexible approach for estimating the effects of covariates on health expenditures.
Our estimation strategy uses sequences of conditional probability functions, similar to those used in discrete time hazard rate analyses, to construct a discrete approximation to the density function of an outcome of interest conditional on exogenous explanatory variables. Once the conditional density function has been constructed, we can examine expectations of arbitrary functions of the outcome of interest and evaluate how these expectations vary with observed exogenous covariates. We demonstrate the features and precision of the conditional density estimation method (and compare it to other commonly used methods) through Monte Carlo experiments and an application to health expenditures using the RAND Health Insurance Experiment data. Overall, we find that the approximate conditional density estimator provides accurate and precise estimates of derivatives of expected outcomes for a wide range of types of explanatory variables.
Gilleskie
Donna B
DB
Department of Economics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. donna_gileskie@unc.edu
Mroz
Thomas A
TA
D000328:Adult; D003669:Deductibles and Coinsurance; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000379:methods*; D006801:Humans; D015273:Insurance Selection Bias; D008297:Male; D008875:Middle Aged; D017059:Models, Econometric; D009010:Monte Carlo Method; D017060:Patient Satisfaction / Q000191:economics* / Q000706:statistics & numerical data; D016016:Proportional Hazards Models; D016896:Treatment Outcome; D014481:United States