This paper studies performance of factor‐based forecasts using differenced and nondifferenced data. Approximate variances of forecasting errors from the two forecasts are derived and compared. It is reported that the forecast using nondifferenced data tends to be more accurate than that using differenced data. This paper conducts simulations to compare root mean squared forecasting errors of the two competing forecasts. Simulation results indicate that forecasting using nondifferenced data performs better. The advantage of using nondifferenced data is more pronounced when a forecasting horizon is long and the number of factors is large. This paper applies the two competing forecasting methods to 68 I(1) monthly US macroeconomic variables across a range of forecasting horizons and sampling periods. We also provide detailed forecasting analysis on US inflation and industrial production. We find that forecasts using nondifferenced data tend to outperform those using differenced data.

中文翻译：

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基于因子预测中的差异与非差异

本文使用差异和非差异数据研究基于因子的预测的性能。推导出和比较来自两个预测的预测误差的近似方差。据报道，使用非差异数据的预测往往比使用差异数据的预测更准确。本文进行模拟以比较两种竞争预测的均方根预测误差。模拟结果表明，使用非差分数据进行预测的效果更好。当预测范围较长且因子数量较多时，使用无差异数据的优势更为明显。本文将这两种相互竞争的预测方法应用于 68 个 I(1) 月度美国宏观经济变量，涵盖一系列预测范围和采样周期。我们还提供对美国通胀和工业生产的详细预测分析。我们发现使用非差异数据的预测往往优于使用差异数据的预测。