Bayesian Forecasting of Economic Time Series
经济时间序列的贝叶斯预测AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确讨论经济时间序列预测、外部事件引起的行为变化及后验模型加权。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
经济时间序列的贝叶斯预测
- 研究问题
- 当重大外部事件可能使经济序列脱离近期行为时，如何检验这种变化，并综合竞争模型的预测来预测未来观测？
- 方法与思路
- 作者用 Jeffreys–Savage 贝叶斯检验评估事件影响，将事件后的观测建模为可交换序列，并按后验模型概率加权预测。
- 材料陈述的贡献
- 摘要称，论文提出事件驱动的预测框架、预测评分与比较方法，并用模拟比较完全贝叶斯预测和预检验预测，但未给出数值结果。
- 阅读关注点
- 应核对事件判定规则、可交换性假设和预测权重，并区分二元示例中的 Pólya 过程、随机游走与摘要所称的局部爆炸行为。
- 阅读边界
- 仅依据所给 Crossref 摘要；事件识别、模拟设计及预测表现数值未说明，二元示例不能直接代表一般经济序列，也无法推断其他版本变化。
原文摘要
A model is suggested to forecast economic time series. This model incorporates some innovative ideas of Harrison and Stevens [20] for building into the forecasting process important external shocks to the systems. Thus the occurrence of possibly significant real-world events may cause a fundamental change in the time series in question. The Jeffreys-Savage (JS) Bayesian theory of hypothesis testing is used to test the hypothesis that a particular event has been such as to free the series from its immediate past behavior. When the event frees the series in this way, then we model the sequence of observations following such an event (until the next such event) as an exchangeable sequence. In the simplest case of 0–1 valued data, such as in recording the ups and downs of the value of a particular commodity or stock, our alternative hypothesis is a Pólya process, and the null hypothesis is a simple random walk (unit roots model) with p = .50. Any exchangeable sequence is strictly stationary, and the observations in the Polya process are positively correlated, which can give rise to “explosive” behavior of the series at isolated time points. We then use the JS theory to predict future observations by taking a weighted average of the optimal predictions for each model, with weights given by the posterior probabilities of the hypotheses. Results of simulation studies are presented which compare the predictive performance of the fully Bayesian method based upon the JS theory with those based upon the “ p -value” or pre-test method. The de Finetti method for scoring predictions is used to assess their empirical performance. A theoretical methodology, which extends the “evaluation game” of Hill [28,37], is developed for comparing predictors.