Investigating the stationarity of insurance premiums: international evidence
Bibliographic record
Abstract
This article explores whether there is support for the stationarity hypotheses of life and non-life insurance premiums during the period 1979–2007 for 40 heterogeneous countries. The stationarity of insurance premiums affects insurance companies’ prediction on their future inflow of premium income, which affects the liquidity of insurance companies and their investment plans and thus is relevant to the insurers’ operation. This article employs the advanced nonlinear panel unit-root test with a sequential panel selection method to classify the entire panel into two groups: stationary countries and non-stationary countries. We apply Monte Carlo simulations to derive empirical distributions of the test, which allows us to correct for the finite-sample bias and to consider the cross-country effects. We find relatively stationary life insurance premiums in countries from the following groups: high-income, Europe, and common law origin; relatively stationary non-life insurance premiums exist in the following groups: low-income, Middle East and Africa, and common law origin. Evidence herein shows that different classifications, including income levels, geographic regions, regionally or economically integrated blocs, and legal system, affect the stationarity of life and non-life insurance premiums.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".