Abstract
Parametric production frontier functions are frequently used in stochastic frontier models, but there do not seem to be any empirical test statistics for its plausibility. To bridge the gap in the literature, we develop two test statistics based on local smoothing and an empirical process, respectively. Residual-based wild bootstrap versions of these two test statistics are also suggested. The distributions of technical inefficiency and the noise term are not specified, which allows specification testing of the production frontier function even under heteroscedasticity. Simulation studies and a real data example are presented to examine the finite sample sizes and powers of the test statistics. The theory developed in this paper is useful for production mangers in their decisions on production.
Highlights
Since the seminal work of Aigner et al (1977) and Meeusen and van den Broeck (1977), stochastic frontier analysis (SFA) has been a very appealing and popular approach for studying productivity and efficiency analysis. Greene (1990) extends the stochastic frontier model by allowing the one-sided component of the disturbance to have a two-parameter Gamma distribution rather than the less flexible half-normal distribution. Greene (2005) extends the model further by using a nonlinear specification
To bridge the gap in the literature, we develop two test statistics based on local smoothing and an empirical process, respectively
The theory developed in this paper is useful for production mangers in their decisions on production
Summary
Since the seminal work of Aigner et al (1977) and Meeusen and van den Broeck (1977), stochastic frontier analysis (SFA) has been a very appealing and popular approach for studying productivity and efficiency analysis. Greene (1990) extends the stochastic frontier model by allowing the one-sided component of the disturbance to have a two-parameter Gamma distribution rather than the less flexible half-normal distribution. Greene (2005) extends the model further by using a nonlinear specification. Parametric SFA models specify the functional form of the production frontier function, m(·), as well as the distributions of the inefficiency term, U , and the independent noise, V. Studying the ‘wrong skewness phenomenon’ in stochastic frontiers (SF), Bonanno et al (2017) propose a more general and flexible specification of the SF model by introducing dependences between the two error components and asymmetry of the random error. These studies above call for the specification testing of the production frontier function.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
