Dear all,

I am attempting to replicate a series of OLS regressions with respect to the effect of CEO overconfidence on net debt issues for my master's thesis and am encountering several issues while doing so. The full OLS regression in the original paper looks as follows:

D =β1+β2*FD+β3*OC+β4*CV+β5*FD x OC+β6*FD x CV+ ε

Where D = net debt issued, FD = Financial deficit, OC = binary overconfidence measure and CV = set of control variables at both CEO and firm level

As you can see, all control variables are included both as level effects and interacted with FD. The coefficient of interest is the interaction term between FD and OC, as I want to know whether overconfidence affects the relative amount of debt issued when a financing deficit occurs in a firm. The authors further add year fixed effects to control for hot equity issuance markets, firm fixed effects to separate effects attributed to overconfident CEOs from time-invariant firm effects and cluster standard errors at firm level. Lastly, they add the interactions of firm fixed effects with FD. I understand that this is done in order to estimate separate intercepts and slopes for each individual firm. The test is supposed to identify the impact of overconfidence on the proportion of the financing deficit covered with debt using only variation that is not confounded by firm-specific effects.

I have replicated this regression in Stata with the following code:

Code:
xtset firmid year
xtreg ndissn i.longholder##c.fidefn c.ceostock##c.fidefn c.ceovest##c.fidefn c.profch##c.fidefn c.tangch##c.fidefn c.qch##c.fidefn c.logsalech##c.fidefn c.booklev##c.fidefn i.year i.firmid##c.fidefn,fe vce(cluster firmid)
Where ndissn = net debt issued, fidefn = financial deficit, longholder = overconfidence measures, i.year = year fixed effects, i.firmid = firm fixed effects and all other variables are control variables.

This is where my issues start. Firstly, in the text the authors mention that when they add the interaction between firm fixed effects and FD, they drop the level effect of FD to avoid collinearity. In their table of results (Table V., attached), the level effect of FD is indeed not reported. However, as far as I understand, dropping the level effect of FD changes the interpretation of the interaction terms of FD and other variables. As FD is interacted with almost every variable in this regression, I do not quite understand what that implies for the interpretation of the results. In my current code, the level effect is reported automatically due to the ## factor variable I use and I am somewhat reluctant to use the # factor variable to eliminate the level effect without understanding what it does to my results.

Secondly, interacting firm fixed effects with FD appears to be a computationally heavy command for Stata. The authors have a dataset of 2385 observations with 263 firms, which is still somewhat feasible, but my dataset contains 7960 observations with 1418 firms. It therefore takes a long time (1hour+) for the code to run, and I have to run it 5 times (adding a control variable/effect every time). As I want to experiment with various changes to my dataset, I would like to know whether there a manner to speed this process up. Perhaps some form of interacting a continuous variable with the xtreg,fe command? I read that for interactions in fixed effects regressions, demeaning the product term is a possibility, but that the standard errors calculated would not be accurate. Is this an option and if so, how would I go about doing that in Stata?

Excuse the lengthy post, but I figured that eliciting answers without adequate background information would prove difficult. I look forward to your insights.

Kind regards,

Marc

Source paper

Malmendier, U., Tate, G. & Yan, J. (2011). Overconfidence and early-life experiences: the effect of managerial traits on corporate finance policies. Journal of Finance, 66(5), 1687-1733.

The analysis of concern spans from page 1711 to page 1714 and starts at the heading "Specification 2: Financing Deficit".

The author's regression results are summarized in Table V. Debt vs Equity (II): Financing Deficit (attached as .png).