We propose a new forecast combination method for panel data vector autoregressions that permit limited forms of parameterized heterogeneity (including fixed effects or incidental trends). Models are fitted using bias-corrected least squares in order to attenuate the effects of small sample bias of forecast loss. We begin by constructing a general estimator of the quadratic forecast risk of the averaged model that is asymptotically unbiased as both $n$ (cross sections) and $T$ (time series) grow large. Armed with this result, we propose a specific weighting mechanism, in which weights are chosen to minimize the estimated quadratic risk of the averaged forecast error. The objective function in this minimization problem is a version of the Mallows C${}_p$ criterion modified for application to the panel data setting. The forecast combination method performs well in Monte Carlo simulations and pseudo-out-of-sample forecasting applications.

我们为面板数据向量自回归提出了一种新的预测组合方法，该方法允许有限形式的参数化异质性（包括固定效应或偶然趋势）。使用偏差校正的最小二乘法拟合模型，以减弱预测损失的小样本偏差的影响。我们首先构建一个渐近无偏的平均模型的二次预测风险的一般估计量，因为$n$ （横截面）和 $吨$（时间序列）变大。有了这个结果，我们提出了一种特定的加权机制，其中选择权重以最小化平均预测误差的估计二次风险。这个最小化问题的目标函数是 Mallows C 的一个版本${}_p$为应用于面板数据设置而修改的标准。预测组合方法在蒙特卡罗模拟和伪样本外预测应用中表现良好。