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Table 8 ETS model information and parameter estimates with their error measures for China

From: Future health expenditure in the BRICS countries: a forecasting analysis for 2035

Indicator

Method

ME

RMSE

MAE

MPE

MAPE

MASE

ACF1

Total Health Expenditure (% of GDP)

ETS(A,A,N)

α = 0.99;β = 0.13

0.04

0.19

0.14

0.75

3.32

0.87

0.07

Total Health Expenditure Per Capita ($PPP)

ETS(M,A,N)

α = 0.99;β = 0.00

6.68

22.26

15.85

0.64

3.57

0.53

0.07

Government Health Expenditure Per Capita ($PPP)

ETS(A,A,N)

α = 0.99;β = 0.20

-0.01

0.08

0.06

0.60

1.93

0.50

0.05

Pre-Paid Private Health Expenditure Per Capita ($PPP)

ETS(A,A,N)

α = 0.77;β = 0.44

0.04

0.12

0.10

-0.75

2.04

0.97

-0.07

Out-of-Pocket Health Expenditure Per Capita ($PPP)

ETS(A,A,N)

α = 0.95;β = 0.00

0.00

0.05

0.04

-0.02

1.18

0.68

0.01

Government Health Expenditure (% of CHE)

ETS(A,A,N)

α = 0.99;β = 0.86

0.00

0.05

0.04

-7.54

19.99

0.39

0.03

Pre-Paid Private Health Expenditure (% of CHE)

ETS(A,Ad,N)

α = 0.70;β = 0.58

0.02

0.12

0.11

-0.93

4.62

0.85

-0.01

Out-of-Pocket Health Expenditure (% of CHE)

ETS(A,A,N)

α = 0.99;β = 0.00

0.00

0.07

0.05

-6.95

26.71

0.67

0.34

  1. ETS Exponential smoothing model; error, trend, and seasonal components are the three major parameters, which can be additive (A), multiplicative (M), or none (N); α = smoothing parameter for the level component; β = smoothing parameter for trend component; ME Mean Error, RMSE Root Mean Squared Error, MAE Mean Absolute Error, MPE Mean Percentage Error, MAPE Mean Absolute Percentage Error, MASE Mean Absolute Scaled Error, ACFI Auto-correlation of Errors at lag 1