Nonparametric Tests for the Independence of Regressors and Disturbances as Specification Tests
借鉴混沌与非线性动力学技术，通过检验回归元与扰动项是否独立来检测序列独立数据模型的设定错误，对遗漏变量、函数形式错误、异方差等问题有良好检验效力，并应用于收入插补模型。
We adapt techniques from the literature on chaos and nonlinear dynamics to detect misspecification in models of serially independent data by checking for dependence between the regressors and disturbances. Our tests are nonparametric in that they determine whether the distribution of the disturbances depends on the regressors without identifying a model of dependence or the distribution of the disturbances. In Monte Carlo simulations we find that these tests have good power against dependence caused by omitted variables, incorrect functional form, heteroskedasticity, and similar problems.We also apply our tests to detect misspecification in models of income imputation. © 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology