近日，我院教授徐吉良的合作论文“Identification of Nonparametric Monotonic Regression Models with Continuous Nonclassical Measurement Errors”（合作者：Yingyao Hu , Susanne Schennach) 被计量经济学顶级期刊 Journal of Econometrics接受发表。
This paper provides sufficient conditions for identification of a nonparametric regression model with an unobserved continuous regressor subject to nonclassical measurement error. The measurement error may be directly correlated with the latent regressor in the model. Our identification strategy does not require the availability of additional data information, such as a secondary measurement, an instrumental variable, or an auxiliary sample. Our main assumptions for nonparametric identification include monotonicity of the regression function, independence of the regression error, and completeness of the measurement error distribution. We also propose a sieve maximum likelihood estimator and investigate its finite sample property through Monte Carlo simulations.