Generalised Partially Linear Regression with Misclassified Data and an Application to Labour Market Transitions

S. Dlugosz, E. Mammen, R. Wilke
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Abstract

We consider the semiparametric generalised linear regression model which has mainstream empirical models such as the (partially) linear mean regression, logistic and multinomial regression as special cases. As an extension to related literature we allow a misclassified covariate to be interacted with a nonparametric function of a continuous covariate. This model is tailormade to address known data quality issues of administrative labour market data. Using a sample of 20m observations from Germany we estimate the determinants of labour market transitions and illustrate the role of considerable misclassification in the educational status on estimated transition probabilities and marginal effects.
错误分类数据的广义部分线性回归及其在劳动力市场转型中的应用
我们考虑了半参数广义线性回归模型的特殊情况,该模型具有主流的经验模型,如(部分)线性平均回归、逻辑回归和多项回归。作为相关文献的延伸,我们允许错误分类的协变量与连续协变量的非参数函数相互作用。该模型是为解决行政劳动力市场数据的已知数据质量问题而量身定制的。使用来自德国的2000万个观察样本,我们估计了劳动力市场转型的决定因素,并说明了教育状况中相当大的错误分类对估计的转型概率和边际效应的作用。
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