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Estimating the density of unemployment duration based on contaminated samples or small samples

机译:根据受污染的样本或小样本估算失业时间的密度

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In estimating a density function for the duration of unemployment, we consider two departures from what would be ideal conditions. If the so-called digit preference effect produces local distortion in observed samples, we can apply a maximum entropy density estimation method. To establish the functional form of the density, we maximize entropy subject to moment restrictions. The global shape of the density is determined by the lower ordered sample moments which are not affected much by the digit preference effect.As a by-product of this method, we can establish the local transition structure of the digit preference effect. As a second case of departure from an ideal condition, we consider coarse sample observations where unemployment duration was observed only for 4, 10, 14, 26, and 52 weeks. Once the unemployment duration density is derived, quintile behavior over time, the Lorenz curve, and the Gini coefficient for the distribution of unemployment duration can be obtained. Finally, we discuss the ramifications of only focusing on the headcount ration of unemployment when other information is available.
机译:在估算失业期间的密度函数时,我们考虑了两个偏离理想条件的情况。如果所谓的数字偏好效应在观察到的样本中产生局部失真,则可以应用最大熵密度估计方法。为了建立密度的函数形式,我们在受力矩限制的情况下最大化熵。密度的整体形状是由较低阶的采样矩确定的,该矩不受数字偏好效应的影响。作为此方法的副产品,我们可以建立数字偏好效应的局部过渡结构。作为偏离理想状况的第二种情况,我们考虑粗略的样本观察,其中仅观察到4、10、14、26和52周的失业时间。一旦得出了失业时间的密度,就可以获得随时间的五分位数行为,洛伦兹曲线和失业时间分布的基尼系数。最后,我们讨论了在可获得其他信息时仅关注失业人数的后果。

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