Nonparametric Estimation and Inference of Production Risk
提出一种基于核方法和自助法的非参数方法，用于估计包含分类和连续变量的随机生产函数，并推断生产风险。蒙特卡洛模拟显示该方法比现有参数和非参数方法更稳健，并用威斯康星州玉米田间试验数据实证分析了转基因品种和种植密度对生产风险的影响。
This paper proposes a nonparametric approach for estimation of stochastic production functions with categorical and continuous variables, and then develops procedures that allow for inference on production risk. The estimation is based on the kernel method and the inference is based on a bootstrapping approach. We establish the asymptotic properties of our proposed estimator. Monte Carlo simulation results suggest that our proposed nonparametric procedure is more robust and outperforms other existing parametric and nonparametric methods. In addition, we empirically illustrate the proposed nonparametric approach using long‐run corn production data from university field trials in Wisconsin that examines the performance of genetically modified corn varieties. Specifically, the proposed nonparametric procedure is used to empirically examine the production risk effects of categorical genetically modified variety variables and a continuous planting density variable.