There is a large and growing body of literature concerned with forecasting time series variables by the use of factor-augmented regression models. The workhorse of this literature is a two-step approach in which the factors are first estimated by applying the principal components method to a large panel of variables, and the forecast regression is then estimated, conditional on the first-step factor estimates. Another stream of research that has attracted much attention is concerned with the use of cross-section averages as common factor estimates in interactive effects panel regression models. The main justification for this second development is the simplicity and good performance of the cross-section averages when compared with estimated principal component factors. In view of this, it is quite surprising that no one has yet considered the use of cross-section averages for forecasting. Indeed, given the purpose to forecast the conditional mean, the use of the cross-sectional average to estimate the factors is only natural. The present paper can be seen as a reaction to this. The purpose is to investigate the asymptotic and small-sample properties of forecasts based on cross-section average–augmented regressions. In contrast to most existing studies, the investigation is carried out while allowing the number of factors to be unknown.

中文翻译：

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使用横截面平均增强时间序列回归进行预测

有大量且不断增长的文献涉及使用因子增强回归模型预测时间序列变量。该文献的主力是两步法，其中首先通过将主成分方法应用于大量变量来估计因子，然后根据第一步因子估计值估计预测回归。另一个引起广泛关注的研究流涉及使用横截面平均值作为交互效应面板回归模型中的公因子估计。与估计的主成分因子相比，这种二次开发的主要理由是横截面平均值的简单性和良好的性能。鉴于此，令人惊讶的是，还没有人考虑使用横截面平均值进行预测。事实上，鉴于预测条件平均值的目的，使用横截面平均值来估计因子是很自然的。本文可以看作是对此的反应。目的是研究基于横截面平均增强回归的预测的渐近和小样本特性。与大多数现有研究相比，调查是在允许未知因素数量的情况下进行的。目的是研究基于横截面平均增强回归的预测的渐近和小样本特性。与大多数现有研究相比，调查是在允许未知因素数量的情况下进行的。目的是研究基于横截面平均增强回归的预测的渐近和小样本特性。与大多数现有研究相比，调查是在允许未知因素数量的情况下进行的。