Proceedings

Estimating the parameters for the proposed model, the relationship between the
dependent variable Environmental Performance Index (marked as EPI) and
independent variable GDP per capita (marked as GDPC), we obtain the following
form for the regression equation (Table 2):
EPI = 67.51565+ 0.329693* GDPC
Table 2. The estimation results for the regression model parameters
Dependent Variable: EPI
Method: Least Squares
Date: 03/26/11 Time: 22:30
Sample: 1 146
Included observations: 146
EPI=C(1)+C(2)*GDPC
Coefficient Std. Error t-Statistic Prob.
C(1) 67.51565 1.095368 61.63743 0.0000
C(2) 0.329693 0.045701 7.214194 0.0000
R-squared 0.265473 Mean dependent var 71.87466
Adjusted R-squared 0.260372 S.D. dependent var 12.83647
S.E. of regression 11.03957 Akaike info criterion 7.654453
Sum squared resid 17549.58 Schwarz criterion 7.695324
Log likelihood -556.7750 Hannan-Quinn criter. 7.671060
F-statistic 52.04459 Durbin-Watson stat 2.102390
Prob(F-statistic) 0.000000
Testing some assumptions made on the parameters of the regression model
We are testing to see whether the variable GDP per capita coefficient is significantly
different from 1.
We define the hypotheses of the statistical test:
H0 : a = 1
H1 : a ≠ 1
We calculate the test statistics using the formula:
a −1
t =
σa
Obtaining: t = -14.68 .
The tabulated value for this test, with 145 degrees of freedom and significance level of
0.001, is 3.291. The calculated value is less than the tabulated value, so we accept null
hypothesis and the GDP per capita coefficient is not significantly different from 1.
Econometric Interpretation
For this model, GDP per capita increase by $ 1,000 (for this data series were divided
by 1000), leads to an improvement in the 0,329 Environmental Performance Index.
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