SEMINONPARAMETRIC BAYESIAN-ESTIMATION OF THE ASYMPTOTICALLY IDEAL PRODUCTION-MODEL

Recently it has been shown that seminonparametric methods can be used to produce 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 Muntz-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.