Table 3 Model estimation by dynamic... | Emerald Publishing

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
Variable	Coefficient	Std. Error	t-Statistic	Prob
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
Variable	Coefficient	Std. Error	t-Statistic	Prob
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