论文标题
当回归系数随时间变化时：一项建议
When regression coefficients change over time: A proposal
论文作者
论文摘要
预测问题中的一种常见方法是从过去的数据中估算最小二乘回归（或其他统计学习模型），然后将其应用于预测未来的结果。一个基本的假设是，过去观察到的相同相关性仍然存在。我们提出了一个未达到此假设的情况的模型：采用来自状态空间文献的方法，我们建模回归系数如何随时间变化。我们的方法可以阐明与预测未来相关的大型不确定性，以及这是由于不断变化的过去动态所致。我们的仿真研究表明，当结果连续时，可以获得准确的估计，但是二进制结果的过程失败。
A common approach in forecasting problems is to estimate a least-squares regression (or other statistical learning models) from past data, which is then applied to predict future outcomes. An underlying assumption is that the same correlations that were observed in the past still hold for the future. We propose a model for situations when this assumption is not met: adopting methods from the state space literature, we model how regression coefficients change over time. Our approach can shed light on the large uncertainties associated with forecasting the future, and how much of this is due to changing dynamics of the past. Our simulation study shows that accurate estimates are obtained when the outcome is continuous, but the procedure fails for binary outcomes.
