Suborno Aditya There are several approaches to remove serial correlation. Alternative approaches include using lagged variables of the dependent variable however literatures suggest its suppressive effect on overall outcome. Other approach is using first difference of variables but by doing this you lose dynamism and critical information from your data and hence you may end up with wrong results.
Hence, it is better to use appropriate methods and techniques to manage serial correlation rather than removing it from the data while estimating panel regression. If it is AR of order 1, Panel regression with Cluster option (xtreg, cluster) should be alright. Or you can estimate using XTREGAR instead of XTREG command that by default manage serial correlation. In that case no need to use cluster option any more.
However, if serial correlation oforder 1 is present alongside heteroskedasticity or/and cross section dependence, using FGLS or PCSE is better. In most literatures, authors do no check for higher order or possibly moving average serial correlation. Thus, managing for only AR1 where your data actually has higher order or moving average autocorrelation, will not reveal efficient or accurate result. However, many authors overlook it and simply estimates equations managing AR1 and gets published. The challenge is determining whether higher order or MA autocorrelation exists in your data after you have already identified autocorrelation of AR1. If it can be identified, there are very specific techniques to manage that in panel estimation.
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