I have a panel data set which I am looking to analyze for relationships/causality using the OLS differences-in-differences method. The panel data includes multiple observations over time for various groups that are most likely correlated to each other. Serial autocorrelation also exists within the data, given the nature of the time series component. My goal is to make my analysis as robust as possible. I’ve researched ways to prevent a biased estimation in the coefficients as much as possible due to these effects, but I am struggling with finding and applying a singular approach.
Example: Lagging the time series component and introducing the lag’d value into the model will minimize the residual autocorrelation attributed to the time series in the model. But won’t account for the between-group relationships. Using clustered standard errors makes the coefficients more robust, but doesn’t seem to deal with the time series autocorrelation component (at least as far as I can tell). Can I obtain the best (i.e. most robust and accurate) result using both methods? Or would doing so further introduce random noise/bias in the model that I’m just missing? Would just one be more appropriate?
Bonus: Is there a Bayesian approach to solve this problem?