2020/07/06 by Rubín, Tomáš
#62M10 (primary) #62M15 #91G30 (secondary) #Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME)
paper · doi:10.48550/arxiv.2007.02763
Viewing a yield curve as a sparse collection of measurements on a latent continuous random function allows us to model it statistically as a sparsely observed functional time series. Doing so, we use the state-of-the-art methods in non-parametric statistical inference for sparsely observed functional time series to analyse the lagged regression dependence of the US Treasury yield curve on US macroeconomic variables. Our non-parametric analysis confirms previous findings established under parametric assumptions, namely a strong impact of the federal funds rate on the short end of the yield curve and a moderate effect of the annual inflation on the longer end of the yield curve.