发表机构
York University（约克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对含结构不稳定性的FAR模型，提出考虑因子估计不确定性、最小化条件MSFE的滚动窗口选择准则，推导其渐近损失有效条件，经模拟实验验证了方法性能。
AI 中文摘要
本文针对存在结构不稳定性时，使用因子增广回归（FAR）模型生成样本外预测的滚动窗口选择问题，构建了一套理论。该理论展示了如何在考虑因子估计不确定性的同时，通过最小化条件均方预测误差（MSFE）来选择滚动窗口。由于条件MSFE不可观测且因子为潜变量，本文提出了该准则的可行版本，并推导了新方法渐近损失有效的条件。一项模拟实验验证了该方法的性能。
英文摘要
The paper develops a theory for selecting the rolling window when generating out-of-sample forecasts with factor-augmented regression (FAR) models in the presence of structural instabilities. It shows how to select a rolling window by minimizing the conditional mean squared forecast error (MSFE) while accounting for uncertainty in factor estimation. Because the conditional MSFE is unobserved and the factors are latent, this paper proposes a feasible version of the criterion and derives conditions under which the new method is asymptotically loss-efficient. A simulation experiment documents the procedure's performance.
