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. 2021 Apr 14;28(33):44949–44972. doi: 10.1007/s11356-021-13639-6

Table 11.

Panel FMOLS, GMM, and quantile regression outputs following Eq. (1)

Dependent variable: CO2-PC Coefficient Std. error t-stat Prob.
Panel FMOLSa,b,c,d,e,f,g
GDP_PC 0.340558 0.039497 8.622437 0.0000
GDP_PCb − 1.88E-06 7.06E-07 − 2.670099 0.0077
Panel GMMa,b,h,i
GDP_PC 0.284117 8.31E-05 3418.667 0.0000
GDP_PCb − 1.37E-06 8.27E-10 − 1653.708 0.0000
Panel quantile regression (0.25)a,b,j,k
GDP_PC 0.530069 0.026371 20.10048 0.0000
GDP_PCb − 7.43E-06 6.80E-07 − 10.91799 0.0000
Panel quantile regression (0.50)a,b,j,k
GDP_PC 0.760386 0.022561 33.70431 0.0000
GDP_PCb − 1.15E-05 9.28E-07 − 12.33841 0.0000
Panel quantile regression (0.75)a,b,j,k
GDP_PC 1.041590 0.040445 25.75333 0.0000
GDP_PCb − 1.00E-05 6.40E-07 − 15.63943 0.0000

aCross-sections included: 36

bTotal panel (balanced) observations: 936

cPanel method: Pooled estimation

dCointegrating equation deterministic: C

eAdditional regressors deterministic: @TREND

fCoefficient covariance computed using the sandwich method (heterogeneous variance structure)

gLong-run covariance estimates (Bartlett kernel, Newey-West fixed

hInstrument specification: @DYN (CO2_PC,-2) GDP_PC

iConstant added to the instrument list

jHuber heterogeneous standard errors and covariance

kSparsity method: Kernel (Epanechnikov) using residuals