Recently it has been shown that seminonparametric methods can be used to produced high-quality approximations to a firm's technology. Unlike the local approximations provided by the conventional class of ‘flexible functional forms’, seminonparametric methods generate global spans within large classes of functions. However, that approach usually spans a much larger space than the neoclassical function space relevant to most production modeling. An exception is the asymptotically ideal model (AIM) generated from the Müntz-Szatz series expansion. Since every basis function in that expansion is within the neoclassical function space, a straightforward method exists for imposing neoclassical regularity, when all factors are substitutes. Since the relevant constraints are inequality restrictions, we implement the approach using Bayesian methods to avoid the problems of sampling distribution truncation that would occur from sampling theoretic methods. We further discuss the relevant extensions that would permit complementary factors, nonconstant returns to scale, and technological change.

The minflex Laurent flexible functional form is a special case of a second-order Laurent series expansion. The minflex Laurent, when constructed in square roots, is called the minflex Laurent (ML) generalized Leontief. The minflex Laurent (ML) translog model is the minflex Laurent in logarithms. We find that the regular region of the ML translog is most often even larger than that of the ML generalized Leontief model, except when substitutability is very low. We previously have shown that the regular region of the ML generalized Leontief is substantially larger than that of the usual translog and generalized Leontief models.