What to do when error term are autocorrelated? For my master thesis I estimated a linear regression. Durbin-Watson test shows, that the error terms are autocorrelated and thus, the estimator could be biased. 
So, what to do now? I found the Cochrane-Orcutt-Transformation but then I get new estimators.. 
What is the right next step?
 A: Cochrane-Orcutt assumes that the autocorrelation in your error term is due to the error term following an AR process. If this is your assumption, then just complete the estimation with the additional second-stage regression (with intercept) of $y_{t} - y_{t-1}\rho$ against $x_{t} - x_{t-1}\rho$ , where $\rho$ is the estimated coefficient in the AR of residuals (i.e. first-order autocorr in residuals of the first regression). For more info look here . This source is exhaustive enough. 
However notice that the assumption of AR structure on residuals of the first regression may be very restrictive and maybe not the case of your data. So for a more general idea of how to solve the problem, you should likely use a ARIMAX model, where the regression allows for a more general ARIMA Error term (if error terms are linearly autocorrelated) or a regression with Garch Error term (if their squares are autocorrelated). For the first look here and here for the second look here. If you find it useful for clarifications also see this.
Clearly this will give you more flexibility in the choice of the assumed structure of dependencies between residuals. You should choose between the 3 (or more) alternatives by following the typical model specification rules: in this case, you have to choose the residual structure that most closely resembles the actual distribution of your first-stage regression residuals (i.e., to be more precise and statistically correct, the one that best removes the dependencies in the standardized innovations in your final MLE model if you use ARIMAX or GARCH regression).
