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Table 7 ETS model information and parameter estimates with their error measures for India

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 = 0.96;β = 0.00

0.00

0.19

0.13

-0.21

3.66

0.88

0.06

Total Health Expenditure Per Capita ($PPP)

ETS(A,A,N)

α = 0.96;β = 0.00

0.16

10.17

7.64

-0.15

3.65

0.92

0.07

Government Health Expenditure Per Capita ($PPP)

ETS(A,A,N)

α = 0.96;β = 0.03

0.00

0.06

0.04

-0.04

0.91

0.95

0.13

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

ETS(A,Ad,N)

α = 0.00;β = 0.00

0.00

0.07

0.05

0.03

0.88

0.66

-0.12

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

ETS(A,A,N)

α = 0.99;β = 0.00

-0.01

0.09

0.06

0.11

1.69

0.88

0.01

Government Health Expenditure (% of CHE)

ETS(A,A,N)

α = 0.99;β = 0.00

0.00

0.11

0.08

-1.04

7.35

0.93

0.11

Pre-Paid Private Health Expenditure (% of CHE)

ETS(A,A,N)

α = 0.00;β = 0.00

0.00

0.10

0.08

-0.12

3.23

0.72

0.08

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

ETS(A,A,N)

α = 0.99;β = 0.00

0.01

0.11

0.07

-3.21

16.15

0.82

0.03

  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