Which of the following are plausible approaches to dealing with residual autocorrelation?

i) Take logarithms of each of the variables

ii) Add lagged values of the variables to the regression equation

iii) Use dummy variables to remove outlying observations

iv) Try a model in first differenced form rather than in levels.
(A)(ii) and (iv) only (B)(i) and (iii) only (C)(i), (ii), and (iii) only (D)(i), (ii), (iii), and (iv)
A