Table 3

Model estimation by dynamic ordinary least squares (DOLS)

Dependent variable: GFG
Sample (adjusted): 1992 2020
Fixed leads and lags specification (lead = 1, lag = 1)
HAC standard errors and covariance (prewhitening with lags = 1, Bartlett kernel, Newey-West fixed bandwidth = 4.0000)
No d.f. Adjustment for standard errors and covariance
VariableCoefficientStd. Errort-StatisticProb
I 0.031556 0.002187 14.42926 2.88E−05 
CO2 −0.06296 0.006283 −10.0198 0.000169 
GDPg −0.00505 0.00246 −2.05166 0.095441 
FDIIN −0.00871 0.001754 −4.96803 0.004219 
FD 0.676,968 0.039048 17.33676 1.17E−05 
POP 0.19015 0.014306 13.29163 4.31E−05 
R-squared 0.9963 Mean dependent var 0.8681 
Adjusted R-squared 0.9738 S.D. dependent var 0.0745 
S.E. of regression 0.0121 Sum squared resid 0.0006 
Dependent variable: GFG
Sample (adjusted): 1992 2020
Fixed leads and lags specification (lead = 1, lag = 1)
HAC standard errors and covariance (prewhitening with lags = 1, Bartlett kernel, Newey-West fixed bandwidth = 4.0000)
No d.f. Adjustment for standard errors and covariance
VariableCoefficientStd. Errort-StatisticProb
I 0.031556 0.002187 14.42926 2.88E−05 
CO2 −0.06296 0.006283 −10.0198 0.000169 
GDPg −0.00505 0.00246 −2.05166 0.095441 
FDIIN −0.00871 0.001754 −4.96803 0.004219 
FD 0.676,968 0.039048 17.33676 1.17E−05 
POP 0.19015 0.014306 13.29163 4.31E−05 
R-squared 0.9963 Mean dependent var 0.8681 
Adjusted R-squared 0.9738 S.D. dependent var 0.0745 
S.E. of regression 0.0121 Sum squared resid 0.0006 
Source(s): Authors’ own work based on E-views 10

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