Input Demands and Inefficiency in U.S. Agriculture
使用马尔可夫链蒙特卡洛方法估计美国农业投入需求方程组，允许非随机技术低效率，并施加凹性约束，评估了技术效率的分布特征。
Abstract 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.