Sorry for the late reply. I was at a conference for the last couple days.
You should not drop any of the dummies without replacing them or without removing all dummies of the same kind completely. Otherwise you would not have a clear reference category. In other words, your reference category would be a mixture of two countries and/or sectors. If you include the dummies for countries and/or sectors depends on your theory behind the estimation. Are different countries and/or sectors expected to confound the effect you'd like to estimate.Since most firms will probably stay in the same country and industry you can think about getting rid of the dummies at all. Other (time-variant) covariates (like firm size) may be more important, but again, this depends on your theory. You may also consider to include dummies for each year (or year groups like decades) if your observations come from different years (maybe the leverage ratio was generally lower in the early 1990s than it is now). You should not just plug different variables into your estimation equation until the desired result comes up. In any case, however, your results so far are not country specific. For that you could either estimate the model for each country separately or to include (multiple) interaction terms between the diff-in-diff variable (which is an interaction already) and the country dummies. Given your sample size, I would not pursue this strategy (just like you planned anyway).
You can estimate the effects for multiple time periods by either estimating the model just for the baseline and the desired point of time in the follow-up period, or you can also use the approach that uses additional interaction terms.This time, you would interact your diff-in-diff variable with an additional dummy that indicates at which point of time in the follow-up period we made this observation. As far as I can see, the -diff- command is not able to do this. Hence, you have to set up your own regression command (I described in #8 how to do so). I don't know how you identify the time of the observations besides your d_time3 dummy. Therefore, I expect you have a year variable. I also expect hat you have a observation for each firm in each year in your dataset. If this is not the case, the syntax below won't work. You should check it anyway. Another assumption is that you only have 3 periods after the treatment. If you have more you need to apply the approach for the additional periods as well. The reference category is t+1 (i.e. the coefficient of diff_in_diff3 shows you the effect in the first treatment period).
Actually it means that the leverage ratio is expected to be negative for this country/sector combination. Clearly, it does not make much sense from a theoretical standpoint. However, this an OLS regression is not restrained to certain values, results of this kind can come up, especially if your categories have very few observations. If you don't use the country and/or sector dummies, there should not be any impossible values for the expected means.
You should not drop any of the dummies without replacing them or without removing all dummies of the same kind completely. Otherwise you would not have a clear reference category. In other words, your reference category would be a mixture of two countries and/or sectors. If you include the dummies for countries and/or sectors depends on your theory behind the estimation. Are different countries and/or sectors expected to confound the effect you'd like to estimate.Since most firms will probably stay in the same country and industry you can think about getting rid of the dummies at all. Other (time-variant) covariates (like firm size) may be more important, but again, this depends on your theory. You may also consider to include dummies for each year (or year groups like decades) if your observations come from different years (maybe the leverage ratio was generally lower in the early 1990s than it is now). You should not just plug different variables into your estimation equation until the desired result comes up. In any case, however, your results so far are not country specific. For that you could either estimate the model for each country separately or to include (multiple) interaction terms between the diff-in-diff variable (which is an interaction already) and the country dummies. Given your sample size, I would not pursue this strategy (just like you planned anyway).
For my thesis (and in general), I am more interested in the time effects; I suspect that a firm's leverage ratio needs some time to develop, and it would be interesting if I could prove that. E.g.: in t+1 leverage decreased with 8%, in t+2 with 6%, and in t+3 with 2%, something like that. I think what I'm missing still is a way to account for changes per year.
Code:
bysort id d_time3 (year): egen tplus = _n if d_time3 ==1 // This command generates a variable that shows if the observation refers to t+1, t+2 and so on (only works if you have a observation for each year after the treatment for each firm - otherwise this command could count a period which is actually t+2 as t+1 if t+1 is not in your dataset) tab tplus, gen(tplus_) // This command generates dummies indicating if the observation comes from t+1, t+2 and so on gen diff_in_diff3_t2 = diff_in_diff3 * tplus_2 // Now we generate the new interaction terms (which are actually three-way interactions since diff_in_diff3 is already a two-way interaction) gen diff_in_dif3_t3 = diff_in_diff3 * tplus_3 reg lvg d_cl d_time3 tplus_2 tplus_3 diff_in_diff3 diff_in_diff3_t2 diff_in_dif3_t3 if d_yr1_2==1, vce(cluster id)
Sebastian, what do you mean with 'expected values for the reference group'? If the control group has a value of -.034 pre-treatment, we can conlude that it has a 3.4% lower leverage than ...? The pre-treatment treatment group? The post-treatment control group? Something else?
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