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Main Authors: Rüter, Lotta, Schienle, Melanie
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2412.02440
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author Rüter, Lotta
Schienle, Melanie
author_facet Rüter, Lotta
Schienle, Melanie
contents 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 six-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.
format Preprint
id arxiv_https___arxiv_org_abs_2412_02440
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Model Determination for High-Dimensional Longitudinal Data with Missing Observations: An Application to Microfinance Data
Rüter, Lotta
Schienle, Melanie
Applications
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 six-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.
title Model Determination for High-Dimensional Longitudinal Data with Missing Observations: An Application to Microfinance Data
topic Applications
url https://arxiv.org/abs/2412.02440