Dear Forum,
I have a -xtreg, fe- regression with a binary variable as independent variable. Further, I have three different dependent variables. My p values are good and significant. My problem is that r squared is very low (about 15%). So I tried to calculate the independent and dependent variables differently, and add and leave out control variables, but the value remains similarly low.
When I look more closely at my independent variable, I notice that it is very often "0". To be exact, 98.7% of the time the independent variable is 0. So could it be that the r squared value here is so low because the independent variable is just so rarely 1? Is it possible to explain this in the master thesis, or should the model be fundamentally changed again? However, I can't change the data, and I can't define the independent variable more broadly.
I am looking forward to your opinions on this!
Thanks and kind regards,
Jana
I have a -xtreg, fe- regression with a binary variable as independent variable. Further, I have three different dependent variables. My p values are good and significant. My problem is that r squared is very low (about 15%). So I tried to calculate the independent and dependent variables differently, and add and leave out control variables, but the value remains similarly low.
When I look more closely at my independent variable, I notice that it is very often "0". To be exact, 98.7% of the time the independent variable is 0. So could it be that the r squared value here is so low because the independent variable is just so rarely 1? Is it possible to explain this in the master thesis, or should the model be fundamentally changed again? However, I can't change the data, and I can't define the independent variable more broadly.
I am looking forward to your opinions on this!
Thanks and kind regards,
Jana

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