I have a balanced panel of firm data, in total 39 firms throughout 21 quarters. These are a subset of S&P100 firms from the period 2013-2020 and were selected based on earnings call transcript availability and respective newspaper coverage. The regression model has cumulative abnormal returns following quarterly earnings calls as the dependent variable and as independent variables: a variable representing economic sentiment, book-to-market, leverage, firm size, EPS surprise and volatility prior to the earnings call.
I know that clustered standard errors rely on asymptotic arguments, therefore it might not be reliable to draw inference on those since the number of clusters along both dimensions is less than the generally recommended number (40-50 approximately). Nevertheless, I could argue that there well might be unobserved components of the error term that clustering would account for.
The case is similar with fixed effects.
So my question is: what is the recommended procedure? Should I just report regression results with and without clustered standard errors and describe the above concerns, or are there any other arguments I could use as to why/why not to cluster and/or use fixed effects in this case? I know the definitions and what these are used for, I'm rather looking for arguments for or against in this specific case.
As usual explain what you did and say what you found. Best wishes, David Booth
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