Assessing Sampling Error in Pseudo‐Panel Models
针对伪面板模型中因单元规模不足导致的估计偏差问题，提出一个综合指标CAWAR，通过蒙特卡洛模拟和实证应用给出其推荐值，帮助研究者确定所需的最小单元规模。
Abstract 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.