Skip to main content

Table 6 ETS model information and parameter estimates with their error measures for Russia

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 = 0.29;β = 0.00

0.00

0.27

0.22

-0.28

4.24

1.05

0.29

Total Health Expenditure Per Capita ($PPP)

ETS(M,A,N)

α = 0.99;β = 0.00

-15.21

65.11

51.61

-1.26

4.42

0.71

0.37

Government Health Expenditure Per Capita ($PPP)

ETS(A,A,N)

α = 0.99;β = 0.00

0.00

0.08

0.07

0.11

3.93

0.77

0.43

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

ETS(A,A,N)

α = 0.99;β = 0.00

0.00

0.15

0.09

-0.02

2.01

1.06

0.02

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

ETS(A,Ad,N)

α = 0.99;β = 0.01

0.00

0.03

0.03

0.02

1.06

0.41

0.39

Government Health Expenditure (% of CHE)

ETS(A,A,N)

α = 0.99;β = 0.00

0.00

0.06

0.05

-1.85

11.47

1.00

0.42

Pre-Paid Private Health Expenditure (% of CHE)

ETS(A,A,N)

α = 0.95;β = 0.00

0.00

0.13

0.08

-0.04

2.75

0.76

0.01

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

ETS(A,A,N)

α = 0.02;β = 0.00

0.00

0.07

0.06

-1.89

10.24

1.08

0.53

  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