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二元选择模型中的误分类AI 解读

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…

误分类模型与劳动力市场动态AI 解读

Measurement error is often a severe problem in longitudinal data such as labor-market flows. Two correction methods have been used for labor-market flows, but the methods tend to adjust the data in opposite directions. One method shows a rigid labor market with long unemployment spells, whereas the other shows the opposite. Because the methods appear theoretically similar, one might interpret the differing results as an indictment of both procedures. This article documents…