For most of the 1990s and 2000s, estimating a stochastic frontier meant running FRONTIER 4.1, Tim Coelli’s Fortran program, and its guide (Coelli, 1996) was where one learnt how. The guide works through a small example: a Cobb-Douglas production frontier for 60 firms with half-normal inefficiency, estimated by maximum likelihood. Its data are distributed with the program and, today, with the R package frontier by Coelli and Henningsen, which implements FRONTIER 4.1 in R.
This post reproduces that example twice: by maximum likelihood with frontier, which returns the guide’s printed estimates, and by Bayesian inference with bsfa. The second is the more interesting exercise, because the two approaches answer slightly different questions, and a sample of 60 is small enough for the difference to show.