Every week, we select a recently published Open Access article to feature. This week’s article is from the Journal of Time Series Analysis and delves into a set of inferential methods application to US and UK yield curves. 

The article’s abstract is given below, with the full article available to read here.

Horváth, L., Kokoszka, P., VanderDoes, J. and Wang, S. (2022), Inference in functional factor models with applications to yield curves. J. Time Ser. Anal., 43: 872-894. https://doi.org/10.1111/jtsa.12642

This article develops a set of inferential methods for functional factor models that have been extensively used in modelling yield curves. Our setting accommodates both temporal dependence and heteroskedasticity. First, we introduce an estimation approach based on minimizing the least-squares loss function and establish the consistency and asymptotic normality of the estimators. Second, we propose a goodness-of-fit test that allows us to determine whether a specific model fits the data. We derive the asymptotic distribution of the test statistics, and this leads to a significance test. A simulation study establishes the good finite-sample performance of our inferential methods. An application to US and UK yield curves demonstrates the generality of our framework, which can accommodate both sparsely and densely observed yield curves.