An application of LASSO and multiple imputation techniques to income dynamics with cross‐sectional data
提出LASSO-PMM方法，用截面数据估计代内收入动态，经拉美和全球43国数据验证，预测结果与实际贫困率、流动性指标等无显著差异，适用于面板数据缺失场景。
This paper introduces, validates, and applies a Least Absolute Shrinkage and Selection Operator with multiple imputation by Predictive Mean Matching (LASSO‐PMM) method to estimate intra‐generational income dynamics from cross‐sectional data. We validate the method using 36 harmonized panel data sets in four Latin American countries and apply it to cross‐section data from 43 countries across the world. Results show that LASSO‐PMM predictions are statistically indistinguishable from actual household poverty rates, mobility indicators, and income or consumption changes. These findings suggest that estimating economic mobility using a LASSO‐PMM approach may accurately approximate actual income dynamics when panel data are unavailable.