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首页> 外文期刊>Journal of applied statistics >Predicting recessions using trends in the yield spread
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Predicting recessions using trends in the yield spread

机译：预测使用产量扩散的趋势的衰退

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摘要

The yield spread, measured as the difference between long- and short-term interest rates, is widely regarded as one of the strongest predictors of economic recessions. In this paper, we propose an enhanced recession prediction model that incorporates trends in the value of the yield spread. We expect our model to generate stronger recession signals because a steadily declining value of the yield spread typically indicates growing pessimism associated with the reduced future business activity. We capture trends in the yield spread by considering both the level of the yield spread at a lag of 12 months as well as its value at each of the previous two quarters leading up to the forecast origin, and we evaluate its predictive abilities using both logit and artificial neural network models. Our results indicate that models incorporating information from the time series of the yield spread correctly predict future recession periods much better than models only considering the spread value as of the forecast origin. Furthermore, the results are strongest for our artificial neural network model and logistic regression model that includes interaction terms, which we confirm using both a blocked cross-validation technique as well as an expanding estimation window approach.
机译：作为长期和短期利率之间的差异的产量传播被广泛认为是经济衰退的最强预测因子之一。在本文中，我们提出了一种增强的衰退预测模型，该模型包含产量扩散的价值的趋势。我们希望我们的模型产生更强的经济衰退信号，因为收益率扩散的稳步下降幅度通常表明与未来的未来业务活动减少相关的悲观主义。我们通过考虑在12个月的滞后的产量水平以及前两季度导致预测起源中的每个季度的延迟的价值来捕获产量传播的趋势，我们使用两个Logit评估其预测能力和人工神经网络模型。我们的研究结果表明，将信息序列的信息与产量级数的型号正确地预测了未来的衰退期比仅考虑到预测原点的扩展值的模型更好。此外，对于我们的人工神经网络模型和逻辑回归模型来说，结果最强，包括交互术语，我们通过阻塞交叉验证技术以及扩展估计窗口方法确认。

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