In non-life insurance, it is essential to understand the serial dynamics and dependence structure of the longitudinal insurance data before using them. Existing actuarial literature primarily focuses on modeling, which typically assumes a lack of serial dynamics and a pre-specified dependence structure of claims across multiple years. To fill in the research gap, we develop two diagnostic tests, namely the serial dynamic test and correlation test, to assess the appropriateness of these assumptions and provide justifiable modeling directions. The tests involve the following ingredients: i) computing the change of the cross-sectional estimated parameters under a logistic regression model and the empirical residual correlations of the claim occurrence indicators across time, which serve as the indications to detect serial dynamics; ii) quantifying estimation uncertainty using the randomly weighted bootstrap approach; iii) developing asymptotic theories to construct proper test statistics. The proposed tests are examined by simulated data and applied to two non-life insurance datasets, revealing that the two datasets behave differently.

在非人寿保险中，在使用纵向保险数据之前了解其序列动态和依赖结构至关重要。现有的精算文献主要集中于建模，通常假设缺乏连续动态和预先指定的多年索赔依赖结构。为了填补研究空白​​，我们开发了两种诊断测试，即串行动态测试和相关性测试，以评估这些假设的适当性并提供合理的建模方向。测试包括以下内容：i）计算逻辑回归模型下横截面估计参数的变化以及索赔发生指标随时间的经验残差相关性，作为检测序列动态的指示；ii) 使用随机加权引导方法量化估计不确定性；iii) 发展渐近理论来构建适当的检验统计量。所提出的测试通过模拟数据进行了检查，并应用于两个非人寿保险数据集，揭示了两个数据集的行为不同。