Input demands and inefficiency in US agriculture

Markov Chain Monte Carlo (MCMC) methods are used to estimate a seemingly un related regression (SUR) system of input demand functions for U.S. agricult ure. Our demand functions have flexible forms and allow for nonrandom techn ical inefficiency. Concavity constraints are imposed at individual data poi nts, and the distributions of measures of relative technical efficiency are constrained to the unit interval. Results are evaluated in terms of charac teristics of the posterior distributions of parameters, measures of relativ e technical efficiency, and other nonlinear functions of the parameters.