SEMIPARAMETRIC ESTIMATION OF PARTIALLY LINEAR MODELS FOR DEPENDENT DATA WITH GENERATED REGRESSORS
研究了含生成回归元的相依数据部分线性模型的半参数估计问题，证明了在弱相依数据下有限维参数仍可达到根号n一致性，适用于理性预期模型、Tobit模型等场景。
In this paper we consider the problem of estimating a semiparametric partially linear model for dependent data with generated regressors. This type of model comes naturally from various econometric models such as a semiparametric rational expectation model when the surprise term enters the model nonparametrically, or a semiparametric type-3 Tobit model when the error distributions are of unknown forms, or a semiparametric error correction model. Using the nonparametric kernel method and under primitive conditions, we show that the [square root]n-consistent estimation results of the finite-dimensional parameter in a partially linear model can be generalized to the case of generated regressors with weakly dependent data. The regularity conditions we use are quite weak, and they are similar to those used in Robinson (1988, Econometrica 56, 931–954) for independent and observed data.