I am currently working on a regression. My aim is to assess whether ESG scores affect the cost of bank loans (variable "AllinDrawn" which is the spread of the loan interest rate over LIBOR, adding any annual (or facility) fee paid to the lender, and it is measured in basis points for each dollar drawn). Below you can find my model:
AllinDrawni,t = f(ESGEnvironmentalPillarScorei,t-1, ESGSocialPillarScorei,t-1, ESGGovernancePillarScorei,t-1, Book_value_of_borrower_assetsi,t-1,Price-to-book_ratioi,t-1, Debt-equity_ratioi,t-1, Altman-z_scorei,t-1, EBITi,t-1, ROEi,t-1, Interest_Coverage_ratioi,t-1, CurrentAssets/CurrentLiabilitiesi,t-1, PPE/TotalAssetsi,t-1,Book_value_of_lender_assetsi,t-1, Dealsizet, Maturityt, Number_of_lenderst,dummy_collateralt, dummy_loantypet,
dummy_corporate_purposet, dummy_country, dummy_time)
My dataset is composed by 1354 observations over the time period 2010-2020. I do not have a balanced panel as different loans are granted to different firms every year; thus, it may occur that the same firm is present throughout the whole period or for one year only. For this reason, I was thinking of using pooled ols as estimation method. In order to understand which is the best option I have used:
1. the command reg combined with the command robust
2. the command reg combined with vce(cluster borrower company id) to account for the cluster effect
3. the command reg combined with vce(robust)
I was also thinking that I could apply a log-transformation to the variables AllinDrawn, Book_value_of_borrower_assets, Maturity and Dealsize as they are positively skewed.
Code:
* Example generated by -dataex-. For more info, type help dataex | |
clear | |
input double(AllinDrawn ESGEnvironmentalPillarScore ESGSocialPillarScore | ESGGovernancePillarScore) |
250 77.5557011795543 64.7579193328731 88.7326388888888 | |
630 5.41978129948686 3.53632478632478 0 | |
275 18.564126394052 65.9194214876033 13.5558069381598 | |
250 31.8097413602534 17.4276859504132 23.1316137566137 | |
275 31.8097413602534 17.4276859504132 23.1316137566137 | |
300 10.7844598613613 6.05716253443526 2.51572327044024 | |
460 21.18 31.11 8.33 | |
200 17.24 31.16 25.01 | |
250 17.5230674087816 74.7279614325069 0 | |
225 44.7532467532467 50.2441860465116 0 | |
225 44.7532467532467 50.2441860465116 0 | |
325 14.8953267935558 4.64531680440771 0 | |
300 69.7174447174446 42.1959358617987 31.4429824561403 | |
325 69.7174447174446 42.1959358617987 31.4429824561403 | |
300 69.7174447174446 42.1959358617987 31.4429824561403 | |
300 69.7174447174446 42.1959358617987 31.4429824561403 | |
325 69.7174447174446 42.1959358617987 31.4429824561403 | |
300 69.7174447174446 42.1959358617987 31.4429824561403 | |
325 69.7174447174446 42.1959358617987 31.4429824561403 | |
37.5 27.3032002127565 36.7112304037709 18.9411157024793 | |
end | |
Code:
* Example generated by -dataex-. For more info, type help dataex | |
clear | |
input double(BookvalueoftotalassetsFirm Pricetobookratio DebttoEquityLeverage | Altmanzscore EBIT ROE InterestCoverageRatio BVofCABVofCLLiquidity PPETotalassetsTangibility) |
24032 1.0432595133933986 1.406099290780142 1.280729860186418 1247 | .06594186576534986 3.681214421252372 2.1813494732122845 .07739680426098536 |
1572.123 1.447634142903065 .07418778253489806 1.2572250390077622 181.934 | .07680436156491312 10.343469568047611 3.861882603848577 .677739591622284 |
786.401 6.312142016730666 .015752023662174805 .5796776708066242 87.059 | .06793264871184676 24.618894123481276 4.380098988998617 .20942369096682228 |
8094.3 1.2947419015853134 1.4887629546248495 1.1027599668902808 525.9 | .029708127253270204 2.4519056261343013 1.8862742241002477 .193617730995886 |
8094.3 1.2947419015853134 1.4887629546248495 1.1027599668902808 525.9 | .029708127253270204 2.4519056261343013 1.8862742241002477 .193617730995886 |
5937.156 2.7769083937119223 .33365353539984816 .36842434323773876 673.24 | .040207092608939246 9.660145073783433 .6675808980320502 .0398877846564921 |
4645.943 1.997013829529877 2.4163164860084185 .13777250388134338 41.896 | .030533362893849964 2.247503143654155 .4604915937554856 .8303532781181344 |
359.4 -6.364957170420625 -.16689280868385345 2.136366165831942 71.5 | .09742114432565924 29.866666666666664 1.4602230483271377 .22120200333889817 |
5234.318 4.109158671361969 .7731561886119267 2.084586320510141 884.737 | .08095669371134429 12.320208982852689 1.1410925133890246 .06600095752684496 |
5407.3 1.427857787689333 1.0857068426536993 2.886581103323285 386.9 | .06020710056697605 5.900889453621346 1.2077922077922076 .3197899136352708 |
5407.3 1.427857787689333 1.0857068426536993 2.886581103323285 386.9 | .06020710056697605 5.900889453621346 1.2077922077922076 .3197899136352708 |
1200.269 6.296495347642471 .7488521977848601 1.183230259216892 150.992 | .02562144501510765 9.576944571457602 2.059774358974359 .024478679362709525 |
2339.679 1.7450898699218107 .34194769086211746 2.1581339576924865 269.709 | .07939763211947916 9.374060374656962 2.7416232869728647 .07157990476471345 |
2339.679 1.7450898699218107 .34194769086211746 2.1581339576924865 269.709 | .07939763211947916 9.374060374656962 2.7416232869728647 .07157990476471345 |
2339.679 1.7450898699218107 .34194769086211746 2.1581339576924865 269.709 | .07939763211947916 9.374060374656962 2.7416232869728647 .07157990476471345 |
2339.679 1.7450898699218107 .34194769086211746 2.1581339576924865 269.709 | .07939763211947916 9.374060374656962 2.7416232869728647 .07157990476471345 |
2339.679 1.7450898699218107 .34194769086211746 2.1581339576924865 269.709 | .07939763211947916 9.374060374656962 2.7416232869728647 .07157990476471345 |
2339.679 1.7450898699218107 .34194769086211746 2.1581339576924865 269.709 | .07939763211947916 9.374060374656962 2.7416232869728647 .07157990476471345 |
2339.679 1.7450898699218107 .34194769086211746 2.1581339576924865 269.709 | .07939763211947916 9.374060374656962 2.7416232869728647 .07157990476471345 |
16081.984 2.736890126343609 .3551863619917197 2.244584617171612 1491.547 | .039283398572413544 12.497946481111873 2.0011704596990167 .13285220281278728 |
end | |
Code:
* Example generated by -dataex-. For more | info, | type | help | dataex |
clear | ||||
input double FacilityAmt int Maturity | ||||
3.000e+09 10 | ||||
1.500e+08 36 | ||||
50000000 36 | ||||
6.500e+08 41 | ||||
3.000e+08 65 | ||||
7.500e+08 36 | ||||
1.000e+08 40 | ||||
240496000 48 | ||||
1.700e+09 40 | ||||
581582051.61 51 | ||||
479417948.39 51 | ||||
1.275e+09 72 | ||||
2.650e+08 60 | ||||
167407404.52 60 | ||||
9521089.21 60 | ||||
3.677e+08 60 | ||||
126560000 60 | ||||
1.0031e+09 72 | ||||
379680000 72 | ||||
1.000e+09 12 | ||||
end | ||||
Code:
reg AllinDrawn ESGEnvironmentalPillarScore ESGSocialPillarScore ESGGovernancePillarScore, robust Linear regression Number of obs = 1,354 F(3, 1350) = 36.61 Prob > F = 0.0000 R-squared = 0.0698 Root MSE = 98.88 --------------------------------------------------------------------------------------------- | Robust AllinDrawn | Coefficient std. err. t P>|t| [95% conf. interval] ----------------------------+---------------------------------------------------------------- ESGEnvironmentalPillarScore | -.0753313 .118659 -0.63 0.526 -.3081074 .1574448 ESGSocialPillarScore | -.3126749 .1829552 -1.71 0.088 -.6715823 .0462324 ESGGovernancePillarScore | -1.01193 .1307176 -7.74 0.000 -1.268362 -.7554986 _cons | 238.8086 7.892187 30.26 0.000 223.3263 254.2909
Code:
reg AllinDrawn ESGEnvironmentalPillarScore ESGSocialPillarScore ESGGovernancePillarScore BookvalueoftotalassetsFirm Pricetobookratio > DebttoEquityLeverage Altmanzscore EBIT ROE InterestCoverageRatio BVofCABVofCLLiquidity PPETotalassetsTangibility, robust Linear regression Number of obs = 1,322 F(12, 1309) = 28.71 Prob > F = 0.0000 R-squared = 0.2597 Root MSE = 86.596 --------------------------------------------------------------------------------------------- | Robust AllinDrawn | Coefficient std. err. t P>|t| [95% conf. interval] ----------------------------+---------------------------------------------------------------- ESGEnvironmentalPillarScore | .0739704 .1128945 0.66 0.512 -.1475035 .2954443 ESGSocialPillarScore | -.1242622 .1538227 -0.81 0.419 -.4260281 .1775037 ESGGovernancePillarScore | -.801108 .1166373 -6.87 0.000 -1.029924 -.5722915 BookvalueoftotalassetsFirm | .0001103 .0000908 1.21 0.225 -.0000678 .0002884 Pricetobookratio | -.8398775 .2880863 -2.92 0.004 -1.405039 -.2747162 DebttoEquityLeverage | 1.745794 .9277623 1.88 0.060 -.0742692 3.565858 Altmanzscore | -14.10079 2.155519 -6.54 0.000 -18.32944 -9.872142 EBIT | -.0065537 .0015351 -4.27 0.000 -.0095652 -.0035421 ROE | -6.562555 2.130634 -3.08 0.002 -10.74239 -2.382725 InterestCoverageRatio | .0207107 .0416538 0.50 0.619 -.0610048 .1024262 BVofCABVofCLLiquidity | -3.882811 2.132499 -1.82 0.069 -8.066301 .3006782 PPETotalassetsTangibility | 5.673791 11.57927 0.49 0.624 -17.04216 28.38974 _cons | 251.7368 8.69042 28.97 0.000 234.6881 268.7855 ---------------------------------------------------------------------------------------------
Thanks a lot in advance for your time.
Kind regards.
Giulia Bonacina
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