I have a panel data set with data on the investments of German firms into other countries. The data are mainly about the target investment enterprises, but it also has some data on the German investor company and sometimes also on the corporote group of the German investor in case the German investor is part of a corporate group. So it has the following structure:
year | country | investor | investment | corporate group of investor | equity of investment | sales of investor | sales of corporate group |
2017 | country1 | inv_ID01 | target_ID01 | group_ID01 | x | x | x |
2017 | country1 | inv_ID01 | target_ID02 | group_ID01 | x | x | x |
2017 | country2 | inv_ID02 | target_ID03 | . | x | x | . |
2017 | country2 | inv_ID03 | target_ID04 | group_ID02 | x | x | x |
2018 | country1 | inv_ID01 | target_ID01 | group_ID01 | x | x | x |
... | ... | ... | ... | ... | ... | ... | ... |
Code:
collapse (sum) equity, by(year country investor)
year | country | investor | equity of investment |
2017 | country1 | inv_ID01 | x |
2017 | country2 | inv_ID02 | x |
2017 | country2 | inv_ID03 | x |
2018 | country1 | inv_ID01 | x |
... | ... | ... | ... |
The idea behind it is the question whether the results of my main analysis change depending on the size of the investor. However, only looking at the investor sales will not give a true picture of the size, because sometimes these investors are only holdings with 0 sales, so it is necessary to look at the whole corporate group. I tried many approaches by now but couldn't figure out a solution.
Does anyone have an idea how to solve this problem?
Best regards,
Anton
0 Response to Advice on changing data to split sample into size categories
Post a Comment