Timeline for Including an endogenous covariate in a regression model as a control to estimate the effect of another variable of interest
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 29, 2021 at 16:10 | vote | accept | Stein Monteiro | ||
| Jun 29, 2021 at 13:52 | comment | added | Richard Hardy | OK, thanks! I think my case is the same as OP's case, as I am following the setup in there. But even if that is not the case, you explanation is helpful. | |
| Jun 29, 2021 at 12:53 | comment | added | tdm | @Richard Hardy What I am saying is that if you first regress $y$ on $x$ and $z_1$ and then $y$ on $z_2$ and $z_1$, then the coefficient on $z_1$ for the second regression is the sum of the coefficients for $x$ and $z_1$ of the first regression. In addition, the coefficient on $x$ in the first regression should be the same as the coefficient on $z_2$ in the second regression. In your case the coefficient on $z_1$ is zero as it is zero in your data generating process. | |
| Jun 29, 2021 at 12:00 | comment | added | Richard Hardy | Regarding Another way to see this..., if I simulate the data according to $y=\beta_0+\beta_1x+e$ with $x=z_1+z_2$ and then regress $y$ on $x$ and $z_1$, the estimated coefficient on $z_1$ is around zero and insignificant -- and far from $\beta_1+\beta_2=\beta_1+0=\beta_1$. Is that in line with what you are saying? | |
| Jun 29, 2021 at 7:08 | history | answered | tdm | CC BY-SA 4.0 |