I think the advice given by Clyde and Carlo is excellent. I'm a finance master's student myself, so maybe I could chime in a little.

The choice between fixed effects and random effects here would mostly come down to whether you're interested in a between estimator or within estimator. In your case, you're interested in finding out what drives a firm's leverage ratio.

A fixed effects estimator essentially shows you a firm's leverage ratio changes when its size, profitability, etc change

**over time**. The Random Effects model allows for the possibility that the the relationship you're modelling is different for each firm.

You are modelling the relationship between a firm's leverage ratio and several firm-specific factors. Unless, you have a reason to believe that this relationship works very different for some firms than for others, a fixed-effects model is the best choice. For example, you could say that the effect of firm size on leverage is smaller for firms in the pharmaceutical sector, because pharmaceutical firms are always highly leveraged no matter what their size (just an illustration). That would mean the relationship between leverage and size is different for different firms and a random-effects estimator could account for this difference.

Unless you have reason to believe the relationship you're documenting is different between the panels, a fixed estimator would be the best choice. My best guess as to your professor's reasoning would be that since you haven't told him of such a reason, he believes the fixed model to be best.

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