A Quantitative Model for the Projection of Health Expenditure

의료비 결정요인 분석을 위한 계량적 모형 고안

A multiple regression analysis using ordinary least square (OLS) is frequently used for the projection of health expenditure as well as for the identification of factors affecting health care costs. Data for the analysis often have mixed characteristics of time series and cross section. Parameters as a result of OLS estimation, in this case, are no longer the best linear unbiased estimators (BLUE) because the data do not satisfy basic assumptions of regression analysis. The study theoretically examined statistical problems induced when OLS estimation was applied with the time series cross section data. Then both the OLS regression and time series cross section regression (TSCS regression) were applied to the same empirical da. Finally, the difference in parameters between the two estimations were explained through residual analysis.