Empirical implementation of ex ante cost functions

Conventional dual methods ignore production risk. Under production risk, estimation of the cost function is complicated even with risk neutrality because the observed actual output is not the decision-relevant expected output. The decision-relevant cost function is not directly observed. An ex ante cost function approach is proposed by embedding the dual distance function computation of expected output within the estimation process. The resulting estimator is consistent and asymptotically efficient. Monte Carlo results indicate that biases and mean squared errors from conventional ex post methods are substantial whereas this approach approximates the efficiency of the directly observable case. An empirical application to U.S. agriculture also produces more plausible estimates than conventional methods.