Hello all,
I am trying to assess the performance of the municipalities from India. For the same, I have computed super-efficiency DEA. I further wish to compute second stage DEA and identify the factors that influence the performance of the municipalities. A popular method, found in this regard is "simarwilson" introduced by Simar and Wilson (2007). However, when I tried to do so, STATA gave a warning that some scores are not bounded in the (0,1] interval and hence, dropped a few observations and presented results for the rest but when I used the "truncreg" command I got results for all the observations. Moreover after reading Stata Journal st0585, I came to know simarwilson can be used only when the dependent variable lies in the (0,1] interval. Since both the "truncreg" and "simarwilson" command compute truncated regression, I wished to know the difference between the two. Moreover, is there any issue if I have used "truncreg" instead of "simarwilson" command to conduct the truncated regression for my analysis.
Thank You
Kind Regards
Shubham
I am trying to assess the performance of the municipalities from India. For the same, I have computed super-efficiency DEA. I further wish to compute second stage DEA and identify the factors that influence the performance of the municipalities. A popular method, found in this regard is "simarwilson" introduced by Simar and Wilson (2007). However, when I tried to do so, STATA gave a warning that some scores are not bounded in the (0,1] interval and hence, dropped a few observations and presented results for the rest but when I used the "truncreg" command I got results for all the observations. Moreover after reading Stata Journal st0585, I came to know simarwilson can be used only when the dependent variable lies in the (0,1] interval. Since both the "truncreg" and "simarwilson" command compute truncated regression, I wished to know the difference between the two. Moreover, is there any issue if I have used "truncreg" instead of "simarwilson" command to conduct the truncated regression for my analysis.
Thank You
Kind Regards
Shubham
