Using Dynamic Linear Models with Changepoints to Understand Trends in the Auto Insurance Industry
Why this work is in the frame
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Bibliographic record
Abstract
Industry-wide auto insurance losses can be difficult to model but are very important for companies to understand. Loss trends can be used to help with ratemaking, on-leveling, and risk management of future losses. We develop a new dynamic linear model with seasonality, regression on congestion, and a linear trend with a changepoint. The changepoint allows us to model structural shifts in the industry, regardless of why they occur (e.g., regulatory, economic, or social changes). We find that the changepoint improves the model fit and will likely lead to improved predictions of future losses; urban congestion best describes the loss process; frequency has generally decreased; and severity has generally increased. Loss cost has increased overall, but it decreased in a significant number of states at the beginning of our time window. We look forward to this model being better able to forecast loss trends in the industry going forward. Address for Correspondence: hartman@stat.byu.edu
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it