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Markov Chain Monte Carlo (MCMC) methods are used to estimate a seemingly unrelated regression (SUR) system of input demand functions for U.S. agriculture. Our demand functions have flexible forms and allow for nonrandom technical inefficiency. Concavity constraints are imposed at individual data points, and the distributions of measures of relative technical efficiency are constrained to the unit interval. Results are evaluated in terms of characteristics of the posterior distributions of parameters, measures of relative technical efficiency, and other nonlinear functions of the parameters.
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O’Donnell et al. (1999) studied this question.
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