In multiple regression with panel data, when calculating cluster robust standard errors, does it matter if there are only a handful of observations in each cluster?
I am looking at if the relationship between Y and X changes after a policy change. I have about 400 firms with 3 years of financial data before the policy change, and 3 years after. I am running the following regression:
Y = Intercept + X + Post * X + controls;
"Post" is a dummy taking the value 1 (or 0) for a year after (or before) the policy change. The coefficient of the them "post * X" would indicate a change in the relation after the new policy is imposed.
To correct for serial correlation within each firm, I use cluster robust standard errors where the cluster is each firm. However, since I only have 6 observations in each cluster (3 before and 3 after the policy change), Do I still need use cluster-robust standard errors? Is clustering a valid concern in this case?...