A popular approach to factor‐augmented panel regressions is the common correlated effects (CCE) estimator of Pesaran (2006). In fact, the approach is so popular that it has given rise to a separate CCE literature. A common assumption in this literature is that the common factors are stationary, which would seem to rule out many empirically relevant cases. Moreover, deterministic factors are typically treated as known, which raises the issue of model misspecification. In the current paper, we show how the conditions placed on the factors in CCE can be made much more general than was previously thought possible. In fact, save for some mild regulatory moment conditions, the factors are essentially unrestricted. One implication of this result is that there is no need to discriminate between deterministic and stochastic factors, but that one can instead treat them all as unknown. This is very convenient for practitioners, because it means that under certain conditions they are spared the problem of having to decide which deterministic terms to include in the model.

中文翻译：

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具有一般未知因素的面板中的CCE

Pesaran（2006）的常用相关效应（CCE）估计器是一种常用的因子增强面板回归方法。实际上，这种方法非常流行，以至于产生了单独的CCE文献。该文献中的一个普遍假设是，共同因素是平稳的，这似乎排除了许多与经验相关的情况。此外，确定性因素通常被视为已知因素，这引起了模型错误指定的问题。在当前的论文中，我们展示了如何使CCE中的因素条件变得比以前认为的要普遍得多。实际上，除了某些温和的调节力矩条件外，这些因素基本上不受限制。此结果的一个含义是，无需区分确定性因素和随机性因素，但是那个人可以将它们全部视为未知。这对于从业者非常方便，因为这意味着在某些条件下，他们可以避免必须确定要在模型中包括哪些确定性术语的问题。