Forecast Encompassing and Parameter Estimation*
研究样本内模型参数估计的不确定性如何影响样本外预测的包含性质，发现真实但估计的数据生成过程的预测通常不包含错误设定模型的预测，尤其在样本量小时，并探讨组合预测的准确性提升空间。
Abstract A desirable property of a forecast is that it encompasses competing predictions, in the sense that the accuracy of the preferred forecast cannot be improved through linear combination with a rival prediction. In this paper, we investigate the impact of the uncertainty associated with estimating model parameters in‐sample on the encompassing properties of out‐of‐sample forecasts. Specifically, using examples of non‐nested econometric models, we show that forecasts from the true (but estimated) data generating process (DGP) do not encompass forecasts from competing mis‐specified models in general, particularly when the number of in‐sample observations is small. Following this result, we also examine the scope for achieving gains in accuracy by combining the forecasts from the DGP and mis‐specified models.