Proceedings

The determination rapport (R-squared) has a small value, almost 26.54% of the
variance of EPI and is explained by the variance of the GDP per capita factor.
Statistical relationship between the endogenous variable and exogenous variable is
weak, which shows that besides the GDPC variable, used to test a causal relationship
between the EPI and GDPC, we also include other variables as the composition of this
indicator includes a number of 25 other influential factors.
The validity of this model can be sustained on the account of low values of probability
and standard error values. The probabilities associated with the t-Student test for
independent variable coefficient is below 5% (0%), thus rejecting the null hypothesis
that the slope of the regression line is insignificantly different from zero, thus
obtaining a properly specified model. Similar results for free term (free term is
significantly different from zero). Between the value of t and F statistics, which
corresponds to the regression slope, we can verify the relationship t ² = F. Durbin-
Watson test, with a value of 2.10, indicating that the residual variables are not auto
related.
Forecast
In order to make a forecast of the EPI variable we have resized the data series by
using a sample of observations from 1 to 120, and starting with the observation 121,
we considered the prediction (Figure 6). The forecast is accurate enough because
RMSE is low (11.33) taking into account that the sample taken into consideration is
quite high, and the Theil coefficient is less than 1 (0.078).
130
120
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100
90
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60
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40
Figure 6. Forecast for EPI variable based on current values
10 20 30 40 50 60 70 80 90 100 110 120
PREVIZIUNI_EPI ± 2 S.E.
~ 1085 ~
Forecast: PREVIZIUNI_EPI
Actual: EPI
Forecast sample: 1 120
Included observations: 120
Root Mean Squared Error 11.33958
Mean Absolute Error 9.242842
Mean Abs. Percent Error 14.55307
Theil Inequality Coefficient 0.078198
Bias Proportion 0.000001
Variance Proportion 0.333530
Covariance Proportion 0.666469
Economic Interpretation
The regression model results obtained by using the application EViews, showed that
GDP per capita influences to a large extent the EPI indicator. By analyzing the
determination ratio (R-squared = 26.54%) we can observe that its amount is quite high
considering the fact that 25 variables are included into the composition of EPI. Given
that developed countries have a very high GDP per capita, an increase of $ 1,000
means (by refering to our average) an increase of the EPI Score by 0.329.
Extensions of the above- used regression model Environmental Performance Index is
a variable depending not only on the classical indicators such as GDP per capita, as