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英文文献：Using the Yield Curve in Forecasting Output Growth and In?flation-用收益率曲线预测产量增长和通货膨胀
英文文献作者：Eric Hillebrand,Huiyu Huang,Tae-Hwy Lee,Canlin Li
英文文献摘要：
Following Diebold and Li (2006), we use the Nelson-Siegel (NS, 1987) yield curve factors. However the NS yield curve factors are not supervised for a specifi?c forecast target in the sense that the same factors are used for forecasting different variables, e.g., output growth or infl?ation. We propose a modifed NS factor model, where the new NS yield curve factors are supervised for a specifi?c variable to forecast. We show it outperforms the conventional (non-supervised) NS factor model in out-of-sample forecasting of monthly US output growth and infl?ation. The original NS yield factor model is to combine information (CI) of predictors and uses factors of predictors (yield curve). The new supervised NS factor model is to combine forecasts (CF) and uses factors of forecasts of output growth or infl?ation conditional on the yield curve. We formalize the concept of supervision, and demonstrate analytically and numerically how supervision works. For both CF and CI schemes, principal components (PC) may be used in place of the NS factors. In out-of-sample forecasting of U.S. monthly output growth and infl?ation, we fi?nd that supervised CF-factor models (CF-NS, CF-PC) are substantially better than unsupervised CI-factor models (CI-NS, CI-PC), especially at longer forecast horizons.
继Diebold和Li(2006)之后，我们使用Nelson-Siegel (NS, 1987)收益率曲线因子。然而，NS收益率曲线因子没有被监管为一个特定的?c预测目标是指用相同的因素来预测不同的变量，例如，产出增长或infl?我们提出了一个修正的NS因子模型，其中新的NS收益曲线因子被监督一个特定的?c变量为预测。我们表明，在对美国月度产出增长和infl的样本外预测方面，它优于传统的(无监督的)NS因子模型。原始的NS收益率因子模型是将预测者的信息(CI)结合起来，使用预测者的因子(收益率曲线)。新的监督NS因素模型是结合预测(CF)和使用预测产出增长或infl?收益取决于收益率曲线。我们将监督的概念形式化，并通过分析和数字来演示监督是如何工作的。对于CF和CI方案，可以使用主成分(PC)来代替NS因子。在美国月度产出增长的样本外预测中?我们,fi吗?另外，有监督的CF-factor模型(CF-NS, CF-PC)比无监督的CI-factor模型(CI-NS, CI-PC)要好得多，尤其是在更长期的预测范围内。
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