论文标题
通过汇总外部信息来最大程度地减少投票后预测错误
Minimizing post-shock forecasting error through aggregation of outside information
论文作者
论文摘要
我们开发了一种预测方法，用于为最近经历了震惊的时间序列提供可信的预测。我们通过借用其他时间序列的知识来实现这一目标，这些知识经历了类似的冲击，观察到后打击后结果。激励三个冲击效应估计器的目的是最大程度地降低平均预测风险。我们提出了降低风险命题，这些命题提供了何时确定方法的条件。提供引导程序和一对一的交叉验证程序，以预先评估我们方法的性能。提供了几个模拟的数据示例，以及预测Conoco Phillips股票价格的真实数据示例，以进行验证和插图。
We develop a forecasting methodology for providing credible forecasts for time series that have recently undergone a shock. We achieve this by borrowing knowledge from other time series that have undergone similar shocks for which post-shock outcomes are observed. Three shock effect estimators are motivated with the aim of minimizing average forecast risk. We propose risk-reduction propositions that provide conditions that establish when our methodology works. Bootstrap and leave-one-out cross validation procedures are provided to prospectively assess the performance of our methodology. Several simulated data examples, and a real data example of forecasting Conoco Phillips stock price are provided for verification and illustration.
