Model determination for high-dimensional longitudinal data with missing observations: an application to microfinance data
提出一种适用于纵向数据缺失观测的多重插补随机lasso方法，用于从213家微型金融机构6年面板数据中识别成功因素，发现员工结构和盈利能力是关键。
Abstract We propose an adaption of the multiple imputation random lasso procedure tailored to longitudinal data with unobserved fixed effects which provides robust variable selection in the presence of complex missingness, high-dimensionality, and multicollinearity. We apply it to identify social and financial success factors of microfinance institutions (MFIs) in a data-driven way from a comprehensive, balanced, and global panel with 136 characteristics for 213 MFIs over a 6-year period. We discover the importance of staff structure for MFI success and find that profitability is the most important determinant of financial success. Our results indicate that financial sustainability and breadth of outreach can be increased simultaneously while the relationship with depth of outreach is more mixed.