  A widespread approach to modelling the interaction between macroeconomic
variables and the yield curve relies on three latent factors usually
interpreted as the level, slope, and curvature (Diebold et al., 2006). This
approach is inherently focused on the conditional mean of the yields and
postulates a dynamic linear model where the latent factors smoothly change over
time. However, periods of deep crisis, such as the Great Recession and the
recent pandemic, have highlighted the importance of statistical models that
account for asymmetric shocks and are able to forecast the tails of a
variable's distribution. A new version of the dynamic three-factor model is
proposed to address this issue based on quantile regressions. The novel
approach leverages the potential of quantile regression to model the entire
(conditional) distribution of the yields instead of restricting to its mean. An
application to US data from the 1970s shows the significant heterogeneity of
the interactions between financial and macroeconomic variables across different
quantiles. Moreover, an out-of-sample forecasting exercise showcases the
proposed method's advantages in predicting the yield distribution tails
compared to the standard conditional mean model. Finally, by inspecting the
posterior distribution of the three factors during the recent major crises, new
evidence is found that supports the greater and longer-lasting negative impact
of the great recession on the yields compared to the COVID-19 pandemic.
