Multiple Imputation of Hierarchical Nonlinear Time Series Data with an Application to School Enrollment Data
层级非线性时间序列的多重插补及其入学率应用
International comparisons of hierarchical time series data sets based on survey data, such as annual country-level estimates of school enrollment rates, can suffer from large amounts of missing data due to differing coverage of surveys across countries and across times. A popular approach to handling missing data in these settings is through multiple imputation, which can be especially effective when there is an auxiliary variable that is strongly predictive of and has a…