We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints. This method can be implemented via a linear programming, and it is thus computationally appealing. We illustrate a usage of our proposed method with an application to the regression kink design (RKD). Econometric analyses based on the RKD often suffer from wide confidence intervals due to slow convergence rates of nonparametric derivative estimators. We demonstrate that economic models and structures motivate shape restrictions, which in turn contribute to shrinking the confidence interval for an analysis of the causal effects of unemployment insurance benefits on unemployment durations.

翻译：我们为在形状限制下建立非对称回归功能的互信带制定了一种新的方法,这种方法可以通过线性编程实施,因此在计算上具有吸引力。我们用回归型设计(RKD)来说明我们拟议方法的使用情况。基于RKD的计量经济学分析往往由于非对称衍生衍生物估计值的趋同速度缓慢而存在广泛的互信间隔。我们证明,经济模式和结构激励着形状限制,这反过来又有助于缩短分析失业保险福利对失业期的因果关系的信任间隔。