• Corpus ID: 19175060

Data-Driven Exploration of Factors Affecting Federal Student Loan Repayment

@article{Luo2018DataDrivenEO,
  title={Data-Driven Exploration of Factors Affecting Federal Student Loan Repayment},
  author={Bin Luo and Qi Zhang and Somya D. Mohanty},
  journal={ArXiv},
  year={2018},
  volume={abs/1805.01586}
}
Student loans occupy a significant portion of the federal budget, as well as, the largest financial burden in terms of debt for graduates. This paper explores data-driven approaches towards understanding the repayment of such loans. Using statistical and machine learning models on the College Scorecard Data, this research focuses on extracting and identifying key factors affecting the repayment of a student loan. The specific factors can be used to develop models which provide predictive… 

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References

SHOWING 1-10 OF 18 REFERENCES
A Trillion Dollar Question: What Predicts Student Loan Delinquencies?
The recent significant increase in student loan delinquencies has generated interest in understanding the key factors predicting the non-performance of these loans. However, despite the large size of
Institutional Accountability: A Comparison of the Predictors of Student Loan Repayment and Default Rates
The federal government holds colleges accountable if too many of their students default on loan repayment, but the default measure traditionally used captures only a fraction of students who are
College on Credit: A Multilevel Analysis of Student Loan Default
This study updates and expands the literature on student loan default. By applying multilevel regression to the Beginning Postsecondary Students survey, four key findings emerge. First, attending
Understanding the Determinants of Debt Burden among College Graduates
This article examines debt burden among college graduates and contributes to previous research by incorporating institutional and state characteristics. Utilizing a combination of national datasets
A Crisis in Student Loans?: How Changes in the Characteristics of Borrowers and in the Institutions They Attended Contributed to Rising Loan Defaults
This paper examines the rise in student loan default and delinquency. It draws on a unique set of administrative data on federal student borrowing matched to earnings records from de-identified tax
Regularization and variable selection via the elastic net
TLDR
It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
Classification and Regression by randomForest
TLDR
random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Regression Shrinkage and Selection via the Lasso
TLDR
A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
TLDR
In this article, penalized likelihood approaches are proposed to handle variable selection problems, and it is shown that the newly proposed estimators perform as well as the oracle procedure in variable selection; namely, they work as well if the correct submodel were known.
A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
Abstract To compute a Bayes factor for testing H 0: ψ = ψ0 in the presence of a nuisance parameter β, priors under the null and alternative hypotheses must be chosen. As in Bayesian estimation, an
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