 Event study difference-in-difference regression

I am currently writing my master thesis, and I have run into some problems where I hope someone can help me. My primary goal is to investigate whether High ESG companies were more resilient than low ESG companies during the Covid-19 stock market crash.

Thus, my difference-in-difference regression looks like this:

reg2 <- plm(Panel\$AR_ESIUWRF2 ~ Panel\$ESG_Treated*Panel\$Post_Covid + Panel\$ESG_Treated*Panel\$Post_Fiscal, data = Panel, index = c("id", "t"), model = "within", effect = "twoways")

Where AR_ESIUWRF2 represents the daily abnormal return, ESG_Treated is a dummy variable equal to one, if the company's ESG score is in the top quartile of the sample, Post_Covid is a dummy variable equal to 1 from the 24th of February till the 31st of march and Post_Fiscal is a dummy variable equal to 1, from the 18th of March till the 31st of March. Thus the methodology should be the same as in the paper. To cluster standard errors by day and firm, is used the command.

summary(reg2, vcov = vcovDC)

providing the following regression results:

Twoways effects Within Model

Note: Coefficient variance-covariance matrix supplied: vcovDC

Call:
plm(formula = Panel\$AR_ESIUWRF2 ~ Panel\$ESG_Treated * Panel\$Post_Covid +
Panel\$ESG_Treated * Panel\$Post_Fiscal, data = Panel, effect = "twoways",
model = "within", index = c("id", "t"))

Balanced Panel: n = 781, T = 63, N = 49203

Residuals:
Min.    1st Qu.     Median    3rd Qu.       Max.
-53.857565  -1.194027  -0.028801   1.145946  36.063013

Coefficients:
Estimate Std. Error t-value Pr(>|t|)
Panel\$ESG_Treated:Panel\$Post_Covid  -0.10660    0.11550 -0.9229   0.3560
Panel\$ESG_Treated:Panel\$Post_Fiscal  0.10777    0.22922  0.4702   0.6382

Total Sum of Squares:    466630
Residual Sum of Squares: 466610
R-Squared:      4.3827e-05
I hope that someone can help me 