Well, unless I'm missing something, your concept of bias is purely metaphysical and cannot be pinned down in the real world. In the equation
the intercept, firm fixed effects, and time fixed effects are not identified. You can add anything you want to the intercept, and then subtract two somethings that add up to that anything from the firm and time fixed effects, respectively, and you have the same model: it makes the same outcome predictions in all cases. So this equation alone cannot serve as a real world model of anything. It requires some kind of constraints to identify the terms. Various constraints can be considered, but I have no sense of what would be appropriate to your situation. Your calculation of bias depends on the specific way in which you constrain the model to identify these components, so, basically, you can make it come out to be anything you want it to be by choosing the corresponding identifying constraints.
There is no such thing as the "true" fixed effects or the "true" constant from a statistical point of view. From an accounting point of view there may be, and if so, that would govern how you constrain the model. Perhaps you can search the accounting literature to learn what the convention is in this regard.
BtP(t)= intercept+ firm fixed effects + time fixed effects+ a* lags of change in stock prices + error(t)
There is no such thing as the "true" fixed effects or the "true" constant from a statistical point of view. From an accounting point of view there may be, and if so, that would govern how you constrain the model. Perhaps you can search the accounting literature to learn what the convention is in this regard.
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