Which of the Following Is NOT a Reason Nonlinear Least

Which of the following is NOT a reason nonlinear least squares is used to estimate an AR(1) model?

A) linear least squares is not possible since the transformation that allows the new error term to be uncorrelated is no longer linear in parameters
B) using OLS to estimate the untransformed model provides incorrect standard errors
C) the algorithmic nonlinear optimization is less complicated to compute when error terms are correlated
D) minimizing the sum of squares of uncorrelated error terms produces an estimator that is unbiased and consistent