Assessing Sampling Error in Pseudo-Panel Models
Assessing Sampling Error in Pseudo-Panel Models
复制标题
评估伪面板模型中的采样误差
DOI：
10.1111/obes.12416
复制
发表时间：
2021
影响因子：
2.5
通讯作者：
Khan R
中科院分区：
文献类型：
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作者：
Khan R
虽然当只有重复的横截面数据可用时，伪面板很有用，但如果单元格大小（分组在一个队列中的个体数量）太少，估计值可能会减弱并遭受抽样误差。然而，对于单元大小需要多大还没有达成共识，建议范围从 100 个到几千个不等。这是由于抽样误差受到细胞大小和队列数据中三种重要变异类型（队列间和队列内以及随时间的推移）的影响。我们将这些组合成一个单一的指标，称为 CAWAR，并使用蒙特卡洛模拟和实证应用来证明其与采样误差的关系。我们为 CAWAR 提供推荐值，超出该值采样误差偏差最小，并且可以根据这些值轻松计算出所需的像元大小。
While pseudo‐panels are useful when only repeated cross‐section data are available, estimates are likely to be attenuated and suffer from sampling error if cell sizes (number of individuals grouped together in a cohort) are too few. However, there is no consensus on how large cell size needs to be, with recommendations ranging from 100 to several thousands. This is due to sampling error being affected by both cell size and three important types of variation in the cohort data (across and within cohorts and over time). We combine these into a single metric, called CAWAR, and demonstrate its relationship to sampling error using Monte Carlo simulations and an empirical application. We produce recommended values for CAWAR beyond which sampling error bias is minimal and from these one can easily calculate the required cell size.