  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.
