Asymptotic theory for the inference of the latent trawl model for extreme values
研究了极值潜拖网模型的统计推断方法及其渐近理论，提出基于自协方差匹配的广义矩估计方法，解决了现有方法低估极值序列相关性的问题，并用伦敦空气污染数据验证了方法的有效性。
Abstract This article develops statistical inference methods and their asymptotic theory for the latent trawl model for extremes, which captures serial dependence in the time series of exceedances above a threshold. We review two methods based on pairwise likelihood and show that they underestimate the serial dependence in the extremes. We propose two generalized method of moments procedures based on auto‐covariance matching to overcome this shortcoming. Out of those four inference approaches, two are single‐stage strategies while the others have two stages, and we provide central limit theorems in the sense of weakly approaching sequences of distributions for all of them. This additional flexibility ensures good behavior between the estimators and estimates of the limiting distribution. In an empirical illustration using London air pollution data, we find that the two‐stage auto‐covariance matching scheme yields a high‐quality inference. It comprises two interpretable steps and correctly captures the serial dependence structure of extremes while performing on par with other methods in terms of marginal fit.