Hi everyone,
I have a problem and I’m not sure whether I’m doing it right. Could anyone help me with this?
I have panel data with N = 124 companies from around the world and T = 7 years (2018–2024). I estimate the following model:
EBITDAit = β0 + β1CO2Emit + β2ESGit + β3CO2EmXCCit +
Controlit + ui + eit
Because I include two dummy variables (Industry and Region), I decided to use a random effects model (REM).
I then tested for:
Here are result:
My questions are:
I have a problem and I’m not sure whether I’m doing it right. Could anyone help me with this?
I have panel data with N = 124 companies from around the world and T = 7 years (2018–2024). I estimate the following model:
EBITDAit = β0 + β1CO2Emit + β2ESGit + β3CO2EmXCCit +
Because I include two dummy variables (Industry and Region), I decided to use a random effects model (REM).
I then tested for:
- Heteroskedasticity using xttest2
- Cross-sectional dependence using xtcdf
- Autocorrelation using xtserial
Here are result:
Code:
. xtreg lnEBITDA lnCO2 ESGScore lnCO2xCC Inflation lnMktCap Size Lev Industry i.region_num, re Random-effects GLS regression Number of obs = 534 Group variable: id Number of groups = 124 R-squared: Obs per group: Within = 0.4239 min = 1 Between = 0.9171 avg = 4.3 Overall = 0.8981 max = 7 Wald chi2(12) = 1564.39 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 -------------------------------------------------------------------------------- lnEBITDA | Coefficient Std. err. z P>|z| [95% conf. interval] ---------------+---------------------------------------------------------------- lnCO2 | .0443468 .0216099 2.05 0.040 .0019922 .0867015 ESGScore | .0021128 .0022001 0.96 0.337 -.0021994 .0064249 lnCO2xCC | .0113222 .0096175 1.18 0.239 -.0075277 .0301722 Inflation | .0086087 .0033217 2.59 0.010 .0020983 .0151191 lnMktCap | .3800489 .0348148 10.92 0.000 .3118132 .4482846 Size | 1.468875 .0980446 14.98 0.000 1.276712 1.661039 Lev | -.0007403 .0033407 -0.22 0.825 -.0072879 .0058073 Industry | -.0019131 .0811607 -0.02 0.981 -.160985 .1571589 | region_num | Australia | .5221762 .1792769 2.91 0.004 .1707999 .8735525 Europe | .4443917 .1094821 4.06 0.000 .2298106 .6589727 North America | .239526 .1074525 2.23 0.026 .0289229 .4501291 South America | .703292 .2437693 2.89 0.004 .225513 1.181071 | _cons | -3.871558 .7487461 -5.17 0.000 -5.339073 -2.404042 ---------------+---------------------------------------------------------------- sigma_u | .3628966 sigma_e | .25249424 rho | .67380799 (fraction of variance due to u_i) --------------------------------------------------------------------------------
Code:
. xtreg lnEBITDA lnCO2 ESGScore lnCO2xCC Inflation lnMktCap Size Lev Industry i.region_num, re vce(cluster id) Random-effects GLS regression Number of obs = 534 Group variable: id Number of groups = 124 R-squared: Obs per group: Within = 0.4239 min = 1 Between = 0.9171 avg = 4.3 Overall = 0.8981 max = 7 Wald chi2(12) = 1383.87 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 (Std. err. adjusted for 124 clusters in id) -------------------------------------------------------------------------------- | Robust lnEBITDA | Coefficient std. err. z P>|z| [95% conf. interval] ---------------+---------------------------------------------------------------- lnCO2 | .0443468 .025415 1.74 0.081 -.0054657 .0941593 ESGScore | .0021128 .0026091 0.81 0.418 -.003001 .0072265 lnCO2xCC | .0113222 .007854 1.44 0.149 -.0040713 .0267158 Inflation | .0086087 .006487 1.33 0.184 -.0041055 .021323 lnMktCap | .3800489 .038064 9.98 0.000 .3054449 .4546529 Size | 1.468875 .1169659 12.56 0.000 1.239626 1.698124 Lev | -.0007403 .0021294 -0.35 0.728 -.0049138 .0034332 Industry | -.0019131 .0711128 -0.03 0.979 -.1412916 .1374655 | region_num | Australia | .5221762 .2244736 2.33 0.020 .0822161 .9621364 Europe | .4443917 .1169048 3.80 0.000 .2152625 .6735208 North America | .239526 .1149305 2.08 0.037 .0142664 .4647856 South America | .703292 .1486125 4.73 0.000 .4120169 .9945671 | _cons | -3.871558 .8817226 -4.39 0.000 -5.599702 -2.143413 ---------------+---------------------------------------------------------------- sigma_u | .3628966 sigma_e | .25249424 rho | .67380799 (fraction of variance due to u_i) --------------------------------------------------------------------------------
- Is using vce(cluster id) with random effects sufficient when heteroskedasticity, autocorrelation, and cross-sectional dependence are all present?
- If not, what would be a more appropriate approach for this situation?
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