Predicting Student Loan Debt: A Hierarchical Time Series Analysis

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dc.contributor.advisorEnsor, Katherineen_US
dc.contributor.authorElsesser, Georgeen_US
dc.date.accessioned2021-07-13T14:14:44Zen_US
dc.date.available2021-07-13T14:14:44Zen_US
dc.date.issued2021en_US
dc.description.abstractIn recent years, and especially in response to the Covid-19 pandemic, much attention has been brought to the issue of rapidly increasing student debt. Yet in the field of time series analysis, there is a dearth of studies examining trends in student loan debt. This is likely due to the impression of simple, yet steep, linear increase in student loan debt over the last decade. However, trends in this type of debt are much more complicated when a complete picture of the hierarchical nature of this data is considered. One objective of this project is to generate accurate forecasts with the impact of Covid-19 in mind not only for outstanding student loan debt, but also for sub-categories of this value based on loan status: such as loans in default or loans in repayment. To facilitate this, traditional hierarchical forecasting methods were compared to newer methods, namely MinT and its recent adaptations. Our findings indicate that MinT forecast reconciliation with the use of structural scaling results in the mostaccurate forecasts across the aggregation structure. Although not the main focus of this study, the second-level forecasts indicate a forecasted 3 1% increase in default rates between the first quarter of 2020 the second quarter of 2022 and a 12% decrease in dollars outstanding for enrolled students during the same time period.en_US
dc.format.extent19 ppen_US
dc.identifier.citationElsesser, George. "Predicting Student Loan Debt: A Hierarchical Time Series Analysis." (2021) Rice University: <a href="https://hdl.handle.net/1911/111013">https://hdl.handle.net/1911/111013</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/111013en_US
dc.language.isoengen_US
dc.publisherRice Universityen_US
dc.rightsCopyright is held by author.en_US
dc.titlePredicting Student Loan Debt: A Hierarchical Time Series Analysisen_US
dc.typeWhite paperen_US
dc.type.dcmiTexten_US
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