Hi, I have a short panel data and after doing unit root ht test, I found that some of the independent variables are nonstationary, therefore I change them into the first difference form. However, I also want to include a lagged term of one explanatory variable, which is stationary and therefore expressed in the normal form. I am using a panel data fixed effect model.
Therefore, my question is
1. does this kind of model make sense (theorectically there is support to add a lagged term)
Y_it= b_0 + b_1X_1it + b_2X_1i(t-1) + b_3D.X_2it + a_i +u_it
2. and if so, how should we interpret b_1, b_2 and b_3?
3. what if b_1 is significant and b_2 is not? however, when we drop the lagged term, b_1 becomes insignificant as well, why is that?
Thank you so much and have a nice day!
Therefore, my question is
1. does this kind of model make sense (theorectically there is support to add a lagged term)
Y_it= b_0 + b_1X_1it + b_2X_1i(t-1) + b_3D.X_2it + a_i +u_it
2. and if so, how should we interpret b_1, b_2 and b_3?
3. what if b_1 is significant and b_2 is not? however, when we drop the lagged term, b_1 becomes insignificant as well, why is that?
Thank you so much and have a nice day!
