We treat producers as successful optimizers. They maximize production, minimize cost, and maximize profits. The conventional econometric techniques build on this paradigm to estimate production/cost/profit function parameters using regression techniques where deviations of observed choices from optimal ones are modeled as statistical noise.
However though every producer may attempt to optimize, not all of them may succeed in their efforts. For example, given the same inputs, and the same technology, some will produce more output than others, i.e., some producers will be more efficient than others. Econometric estimation techniques should allow for the fact that deviations of observed choices from optimal ones are due to two factors: failure to optimize i.e., inefficiency due to random shocks.
Stochastic Frontier Analysis or SFA is one such technique to model producer behavior
SFA produces efficiency estimates or efficiency scores of individual producers. Thus one can identify those who need intervention and corrective measures
Since efficiency scores vary across producers, they can be related to producer characteristics like size, ownership, location, etc. Thus one can identify source of inefficiency.
SFA provides a powerful tool for examining effects of intervention. For example, has efficiency of the banks changed after deregulation? Has this change varied across ownership groups?
Dear Mousumi
Thank you very much for your answer. Is there any chance you could explain bit further about SFA. I am struggling to understand about the random error and inefficiency term and their distributional assumption. It says inefficiency follows a half normal or exponential distribution and the noise term follows normal distribution. What is the meaning of this ?
Your help in this matter is highly appreciated.
Looking forward to hear from you.
You can go through the following links
http://www.igidr.ac.in/conf/finwrk/workshop2.pdf
http://pages.stern.nyu.edu/~wgreene/FrontierModeling/SurveyPapers/LIMDEP-Chapter33.pdf
Hello, you need to posit/test that the disturbance component of the composed error term follows one of the following distributions: half-normal, truncated normal, exponential, or gamma distribution. It is also important to take care of heteroscedasticity in your model.
Useful links:
Article Stochastic Frontier Analysis
Article Stochastic Frontier Analysis Using Stata
Book A Practitioner's Guide to Stochastic Frontier Analysis Using STATA
Sorry Smriti, the distributional assumptions I mentioned above apply to the inefficiency term and not the disturbance component of the error term. The random disturbance term follows a normal distribution. I got that mixed up.
do one need to perform heteroscadaticity or stationarity test before running SFA for efficiency test on panel data
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