Otherwise, the solutions above might be OK, but it is hard to say without knowing your research question. If you are trying to estimate the coefficients for the country dummies, then certainly not. This all begs the question of whether this exercise makes any sense at all. Since you would ignore any output on the dummies, you might as well use a model like areg or xtreg since they run more quickly.Īlthough there are many applications where bootstrapping with clusters would work fine, the problem here is the inclusion of cluster dummies in the regression. (The coefficients for the two dummies naturally turn out to be identical or near-identical.) However, the coefficients in this output are are completely meaningless because bscountry "2" will be different countries in different bootstrap iterations. If a country is drawn twice then there are two dummies. With the idcluster() option, each country that is drawn in a bootstrap sample is given its own ID number. bootstrap, cluster(country) idcluster(bscountry) noisily: regress mvalue kstock i.bscountry One way think about what is going on is that it is analogous to this. After the latter has been executed, the estat bootstrap postestimation command displays a table. If you don't care about the coefficient dummies you could use another command that simply absorbs the fixed effects and only reports the coefficients on the other independent variables (e.g., areg or xtreg). Stata implements the bootstrap through the bootstrap prefix. If you are really trying to estimate the coefficients on the country dummies, you are going to have to find another approach than bootstrapping with K clusters if K is the number of countries. This should be expected and makes a lot of sense - the country dummies could =0 for all observations in the bootstrap sample if the country was not drawn! In each regression you can see that some countries dummies are being omitted due to collinearity. regress mvalue kstock i.bscountry, vce(bootstrap, cluster(country) noisily)Įrrors are generated because some coefficients are missing when the regression runs with particular bootstrap samples. You can diagnose what is going on with the vce(, noisily) sub-option. Not all "countries" are included in the dataset for each bootstrap repetition.
The problem is that you cannot identify each coefficient in each regression in each bootstrap sample.
The problem here is not with your coding, but is conceptual.
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