# How to manually calculate the standard errors of autoregressive terms and sigma in regression equation?

I have fitted a censored regression model in R which whose outputs look like this

library(carx)
SAmodel <- carx(y=SAdata$CC, x=SAdata[,c("CPI","LI","FDI","FPI", "OI")], lcl=1.996866, p=2, CI.compute = FALSE, CI.level = 0.95) summary(SAmodel) Call: carx.default(y = SAdata$CC, x = SAdata[, c("CPI", "LI", "FDI", "FPI",
"OI")], lcl = 1.996866, p = 2, CI.compute = FALSE, CI.level = 0.95)

Coefficients Estimate
CPI     0.0804
LI     -0.0088
FDI    -0.0395
FPI    -0.0166
OI      0.0488
AR1     1.6324
AR2    -0.7097
sigma   0.4286

AIC:
[1] -72.92045


The outputs come with no standard errors and no confidence intervals, those can be obtained by bootstrapping which could take tremendous time, probably more than a day, so I decided to manually calculate the standard errors. I know how standard errors are calculated for the coefficients in multiple regression and based on that I did the following calculations

#Calculating the residuals of the fitted model
SAres = residuals(SAmodel,type="raw")
# Find the sum of the squared residuals
S <- sqrt(rss / (length(SAres) - length(SAmodel$$coefficients))) # Make the X matrix; a column of 1s for the intercept and one for each variable X <- cbind(rep( nrow(SAdata)), SAdata$$CPI, SAdata$$LI,SAdata$$FDI, SAdata$$FPI, SAdata$$OI)