@Wooldridge
My main model is
y1 = b0 + b1*y2 +b2*(y2)^2 + b3*(Other_Controls) +ui
I assume that y2 is endogenous, and I have z1 as an instrument for y2. As 2SLS cannot be used in this model because of its non-linearity, I am using the Control Function Approach.
From the ppt and the paper written by Wooldridge(2015), I could infer that the steps are as follows-
xtreg y2 z1 z1^2 Other_Controls, fe cluster(i)
From this model I predict the Residuals.
Then I run the main model
xtreg y1 y2 y2^2 Residuals Other_controls, fe cluster(i)
If the residuals come out as significant, then there is a problem of endogeneity.
I am a little confused about steps as there is no other blog specifically checking for the endogeneity of a squared Explanatory Endogenous Variable. Am I doing it correctly? Any other suggestions are welcome.
My main model is
y1 = b0 + b1*y2 +b2*(y2)^2 + b3*(Other_Controls) +ui
I assume that y2 is endogenous, and I have z1 as an instrument for y2. As 2SLS cannot be used in this model because of its non-linearity, I am using the Control Function Approach.
From the ppt and the paper written by Wooldridge(2015), I could infer that the steps are as follows-
xtreg y2 z1 z1^2 Other_Controls, fe cluster(i)
From this model I predict the Residuals.
Then I run the main model
xtreg y1 y2 y2^2 Residuals Other_controls, fe cluster(i)
If the residuals come out as significant, then there is a problem of endogeneity.
I am a little confused about steps as there is no other blog specifically checking for the endogeneity of a squared Explanatory Endogenous Variable. Am I doing it correctly? Any other suggestions are welcome.
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