论文标题
结果变量中的非经典测量误差
Nonclassical Measurement Error in the Outcome Variable
论文作者
论文摘要
我们研究结果变量中具有一般形式的非经典测量误差形式的半/非参数回归模型。我们显示了该模型与广义回归模型的等效性。我们的主要识别假设是在观察到的和未观察到的真实结果之间的非线性关系中的特殊回归型限制和单调性。然后在未知链路函数的归一化中获得非参数识别，这是经典测量误差案例的自然扩展。我们提出了回归函数的新型筛分估计量，并确定其收敛速度。 在Monte Carlo模拟中，我们发现我们的估计器纠正了非经典测量误差引起的偏差，并提供了数值稳定的结果。我们将我们的方法应用于德国社会经济小组（SOEP）的调查数据来分析股票市场期望的信念形成，并在主观信念数据中找到非经典测量错误的证据。
We study a semi-/nonparametric regression model with a general form of nonclassical measurement error in the outcome variable. We show equivalence of this model to a generalized regression model. Our main identifying assumptions are a special regressor type restriction and monotonicity in the nonlinear relationship between the observed and unobserved true outcome. Nonparametric identification is then obtained under a normalization of the unknown link function, which is a natural extension of the classical measurement error case. We propose a novel sieve rank estimator for the regression function and establish its rate of convergence. In Monte Carlo simulations, we find that our estimator corrects for biases induced by nonclassical measurement error and provides numerically stable results. We apply our method to analyze belief formation of stock market expectations with survey data from the German Socio-Economic Panel (SOEP) and find evidence for nonclassical measurement error in subjective belief data.
