Testing the impacts on inefficiency in a semiparametric stochastic frontier model
针对半参数随机前沿模型中无效率项的外生决定因素，提出两种显著性检验方法，通过条件矩约束和随机过程构造检验统计量，并用乘子自助法模拟临界值，解决遗漏变量偏差和估计效应问题。
.This article is concerned with the significance testing of the effects of exogenous determinants upon the one-sided deviation term of a semiparametric stochastic frontier model. Two significance tests for all or a subset of the determinants of inefficiency are proposed. The tests are based on conditional moment restrictions and stochastic processes, with critical values simulated using the computationally fast multiplier bootstrap procedures. Our testing methodology addresses the omitted variable bias that arises naturally in stochastic frontier models when accommodating the determinants of inefficiency and accounts for the estimation effects that appear when using the estimated composite error in constructing the test statistics. We investigate the theoretical properties of the proposed tests and justify the validity of the multiplier bootstrap procedures. The tests are finally illustrated through simulation experiments and two empirical examples. In particular, the analysis from Taiwanese manufacturing supports the financing constraint hypothesis through the significant effect of cash flow, while the analysis from Indian rice production highlights the importance of schooling and farming experience in reducing farm inefficiency.