Details
Date: Feb 10, 2026
Location: Waltham, MA - USA
Abstract
The consumer auto lending market in the United States operates on a massive scale: consumer auto asset-backed securities~(ABS) issuance tops $200 billion and total consumer automobile debt exceeds $1,400 billion. Despite this scale, there is a lack of ready-to-use software implementing rigorous statistical methods for the purposes of drawing inference on the lifetime distribution of individual consumer loans sampled from ABS.@ Such tools would be beneficial to ABS investors because the source of the ABS level cash flow is the sum of its underlying individual assets. Hence, investors require precise time-to-event distribution estimates to accurately model aggregate ABS trust level performance. Furthermore, the convenience of statistical software designed for this purpose would be valuable in the high-paced environment of fixed-income trading. The ABS data is nontrivial to analyze, however, and we begin by reviewing statistical methods for left-truncated, right-censored, discrete time-to-event data, including competing risks. Next, we introduce asymptotic distributions for the cumulative and probability mass functions for the time-to-event distribution estimates. We then present the abslife package for R, designed specifically to analyze such data. We illustrate the package’s utility through an analysis of 275,948 consumer auto loans spanning four distinct ABS bonds, demonstrating how abslife enables financial analysts to perform robust inference and produce meaningful visualizations for discrete time-to-event data.