Nonparametric Identification under Discrete Variation
给出了弱条件下非参数区间识别结构函数局部特征的方法，该函数依赖离散内生变量且不可分离，可用于分析Angrist和Krueger关于教育回报的研究。
This paper provides weak conditions under which there is nonparametric interval identification of local features of a structural function that depends on a discrete endogenous variable and is nonseparable in latent variates. The function delivers values of a discrete or continuous outcome and instruments may be discrete valued. Application of the analog principle leads to quantile regression based interval estimators of values and partial differences of structural functions. The results are used to investigate the nonparametric identifying power of the quarter-of-birth instruments used in Angrist and Krueger's 1991 study of the returns to schooling. Copyright The Econometric Society 2005.