论文标题
预测经济衰退的主要指标：为期10年的3个月财政收益率传播
Forecasting the Leading Indicator of a Recession: The 10-Year minus 3-Month Treasury Yield Spread
论文作者
论文摘要
在这篇研究论文中，我应用了各种计量经济学时间序列和两个机器学习模型来预测收益率差异的日常数据。首先，我将产量曲线分解为其主要成分，然后使用Vasicek模型模拟了产量扩散的各种路径。在构建单变量ARIMA模型和多元模型（例如Arimax，var和长期内存）之后，我校准了根平方误差，以测量结果与当前值的差异。通过脉冲响应功能，我测量了各种冲击对差异差异差的影响。结果表明，副单变量Arima模型的表现优于参数化的VAR方法，而具有多变量数据的复杂LSTM的性能与简单的Arima模型同样出色。
In this research paper, I have applied various econometric time series and two machine learning models to forecast the daily data on the yield spread. First, I decomposed the yield curve into its principal components, then simulated various paths of the yield spread using the Vasicek model. After constructing univariate ARIMA models, and multivariate models such as ARIMAX, VAR, and Long Short Term Memory, I calibrated the root mean squared error to measure how far the results deviate from the current values. Through impulse response functions, I measured the impact of various shocks on the difference yield spread. The results indicate that the parsimonious univariate ARIMA model outperforms the richly parameterized VAR method, and the complex LSTM with multivariate data performs equally well as the simple ARIMA model.
