Order selection in GARMA models for count time series: a Bayesian perspective
计数时间序列 GARMA 模型的贝叶斯阶数选择AI 解读 ↓
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计数时间序列 GARMA 模型的贝叶斯阶数选择
- 研究问题
- 面对计数时间序列 GARMA 模型中信息准则选阶表现不佳的问题，贝叶斯方法如何联合处理模型阶数选择及参数不确定性？
- 方法与思路
- 作者采用可逆跳跃马尔可夫链蒙特卡洛开展选阶，以模拟考察点与区间推断、先验敏感性及采样设置，并提供巴西数据应用。
- 材料陈述的贡献
- 摘要提出贝叶斯选阶方案，系统考察其有限样本表现及先验、预热和抽稀设置的影响，并以汽车生产和公交车出口数据展示应用。
- 阅读关注点
- 应核对跨阶数跳跃的构造、阶数与参数先验、后验模型概率，以及模拟中的识别正确率和链混合诊断是否支持方法表现。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未给出选阶准确率、后验区间覆盖率或收敛诊断，不能认定全面优于信息准则。
原文摘要
Estimation in GARMA models has traditionally been carried out under the frequentist approach. To date, Bayesian approaches for such estimation have been relatively limited. In the context of GARMA models for count time series, Bayesian estimation achieves satisfactory results in terms of point estimation. Model selection in this context often relies on the use of information criteria. Despite its prominence in the literature, the use of information criteria for model selection in GARMA models for count time series have been shown to present poor performance in simulations, especially in terms of their ability to correctly identify models, even under large sample sizes. In this study, we study the problem of order selection in GARMA models for count time series, adopting a Bayesian perspective through the application of the Reversible Jump Markov Chain Monte Carlo approach. Monte Carlo simulation studies are conducted to assess the finite sample performance of the developed ideas, including point and interval inference, sensitivity analysis, effects of burn-in and thinning, as well as the choice of related priors and hyperparameters. Two real-data applications are presented, one considering automobile production in Brazil and the other considering bus exportation in Brazil before and after the COVID-19 pandemic, showcasing the method's capabilities and further exploring its flexibility.