Dear All,

I am mostly an R user. But since I have not found a way yet to reproduce generalised residuals in R (link), I am using Stata for my estimation.

The jist is as follows: I run an oprobit on my ordinal EEV, I get the generalised residual (apparently that is just one residual per observation, instead of on per level?). I add the residual with the EEV in the second stage. And finally I calculate the AME/APE.

ordinal_var is an ordinal variable with four levels
depvar is the dependent variable with values ranging between (and including) 0 and 1.

Code:
 oprobit ordinal_var instrumentalvar1 instrumentalvar2
  drop residual
  predict residual, score
  fracreg logit depvar i.ordinal_var residual, vce(robust)
  margins(ordinal_var)
One reason I am posting this, is because I was a little bit surprised about the output from margins(ordinal_var). Everything looks a little bit weird. Crazy significant, but there is hardly any difference in the effect of all of the levels. Also, I was kind of expecting three marginal effects and not 4.

Could anyone let me know if there is anything wrong with my code?


Code:
  ----------------------------------------------------------------------------------
                   |                Delta-method
                   |    Margin     Std. Err.    z     P>|z|        [95% Conf. Interval]
  -----------------+----------------------------------------------------------------
  ordinal_var|
                0  |   .8752151   .0098138    89.18   0.000     .8559803    .8944498
                1  |   .8434355   .0045137    186.86  0.000     .8345888    .8522822
                2  |   .8412551   .0091412    92.03   0.000     .8233386    .8591716
                3  |   .8268829   .0203199    40.69   0.000     .7870567    .8667091
  ----------------------------------------------------------------------------------


References:

Chiburis, R., & Lokshin, M. (2007). Maximum Likelihood and Two-Step Estimation of an Ordered-Probit Selection Model. The Stata Journal, 7(2), 167–182. https://doi.org/10.1177/1536867X0700700202

Terza J. V, Basu A., Rathouz, P.J. (2008) Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. Journal of health economics 27 (3), 531-543, 2008

Vella, F. (1993). A Simple Estimator for Simultaneous Models with Censored Endogenous Regressors. International Economic Review, 34(2), 441-457. doi:10.2307/2526924

Wooldridge,JM. (2014) Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables. Journal of Econometrics Volume 182, Issue 1, September 2014, Pages 226-234