A Generalized Model of Misclassification Errors and Labor Force Dynamics
使用潜变量方法建模美国劳动力市场转换，通过特征值分解识别误分类概率和真实转换概率，发现考虑误差持续性后实际劳动力流动性被低估。
We study the US labor market transitions using a latent variable approach, explicitly modeling the persistent misclassification process and the non-Markovian nature of the underlying true labor force dynamics. A closed-form global identification for misclassification probabilities and labor transition probabilities is established through an eigenvalue-eigenvector decomposition. Contrary to existing studies, our empirical results suggest that the observed data have understated the true mobility in labor force statuses after we account for persistence in both the misclassification errors and the latent true labor force dynamics.