Special thanks to the Amazing Kit Baum, -sfkk- is now available on SSC. You can install -sfkk- from SSC by entering the following command in Stata:

sfkk fits endogenous stochastic production or cost frontier models following the methodology provided by Karakaplan and Kutlu (2015). sfkk provides estimators for the parameters of a linear model with a disturbance that is assumed to be a mixture of two components: a measure of inefficiency which is strictly nonnegative and a two-sided error term from a symmetric distribution. sfkk can handle endogenous variables in the frontier and/or the inefficiency, and the sfkk estimates outperform the standard frontier estimates that ignore endogeneity. See Karakaplan and Kutlu (2015) for a detailed explanation of their methodology and empirical analyses.

Karakaplan and Kutlu (2017) provides the econometric methodology that -sfkk- is based on. This paper is published in the Economics Bulletin: http://www.accessecon.com/Pubs/EB/20...V37-I2-P79.pdf Karakaplan (2017) provides the -sfkk- program itself with some examples. This paper is published in the Stata Journal: http://www.stata-journal.com/article...article=st0466

-sfkk- help file provides several examples and they can be viewed by typing the following command in Stata after installing the -sfkk- package:

Below is an example of an -sfkk- output:

Code:

ssc install sfkk

Karakaplan and Kutlu (2017) provides the econometric methodology that -sfkk- is based on. This paper is published in the Economics Bulletin: http://www.accessecon.com/Pubs/EB/20...V37-I2-P79.pdf Karakaplan (2017) provides the -sfkk- program itself with some examples. This paper is published in the Stata Journal: http://www.stata-journal.com/article...article=st0466

-sfkk- help file provides several examples and they can be viewed by typing the following command in Stata after installing the -sfkk- package:

Code:

help sfkk

Code:

. use http://www.mukarakaplan.com/files/sfkkprod.dta, clear . sfkk y x1 x2 z1, prod u(z2) en(z1 z2) i(iv1 iv2) delve nicely header beep compare 18 Jun 2015 12:09:18 ENDOGENOUS STOCHASTIC PRODUCTION FRONTIER MODEL (Model EN) Dependent Variable: y Frontier Variable(s): x1 x2 z1 U Variable(s): z2 W Variable(s): Endogenous Variable(s): z1 z2 Excluded Instrument(s): iv1 iv2 Exogenous Variable(s): iv1 iv2 x1 x2 Delving into the problem... initial: log likelihood = 709.21899 rescale: log likelihood = 709.21899 rescale eq: log likelihood = 709.21899 Iteration 0: log likelihood = 709.21899 Iteration 1: log likelihood = 713.90317 Iteration 2: log likelihood = 713.98024 Iteration 3: log likelihood = 713.98037 Iteration 4: log likelihood = 713.98037 Analyzing the exogenous comparison model... Table: Estimation Results ---------------------------------------------------------------- Model EX Model EN ---------------------------------------------------------------- Dep.var: y Constant 0.475*** (0.017) 0.631*** (0.032) x1 0.215*** (0.019) 0.186*** (0.031) x2 0.089*** (0.021) 0.132*** (0.033) z1 -0.355*** (0.022) -0.747*** (0.111) ---------------------------------------------------------------- Dep.var: ln(sigmau²) Constant -3.786*** (0.602) -7.096*** (0.828) z2 -19.715 (10.599) 8.207*** (1.468) ---------------------------------------------------------------- Dep.var: ln(sigmav²) Constant -4.236*** (0.073) ---------------------------------------------------------------- Dep.var: ln(sigmaw²) Constant -4.819*** (0.177) ---------------------------------------------------------------- eta1 (z1) 0.457*** (0.114) eta2 (z2) 0.664*** (0.057) ---------------------------------------------------------------- eta Endogeneity Test X2=155.15 p=0.000 ---------------------------------------------------------------- Observations 500 500 Log Likelihood 342.86 713.98 Mean Prod Efficiency 0.9821 0.9152 Median Prod Efficiency 0.9946 0.9364 ---------------------------------------------------------------- Notes: Standard errors are in parentheses. Asterisks indicate significance at the 0.1% (***), 1% (**) and 5% (*) levels. ----------------------------------------------------------------

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