Misclassification in binary choice models
研究了二元因变量误分类导致的估计偏差，推导了参数模型中的渐近偏差公式，并通过模拟和验证数据证明其准确性，发现限制误分类与协变量相关会加剧偏差。
Bias from misclassification of binary dependent variables can be pronounced. We examine what can be learned from such contaminated data. First, we derive the asymptotic bias in parametric models allowing misclassification to be correlated with observables and unobservables. Simulations and validation data show that the bias formulas are accurate in finite samples and in most situations imply attenuation. Second, we examine the bias in a prototypical application. Erroneously restricting the covariance of misclassification and covariates aggravates the bias for all estimators we examine. Estimators that relax this restriction perform well if a model of misclassification or validation data is available.