Inference in functional factor models with applications to yield curves
本文为广泛用于收益率曲线建模的函数型因子模型开发了一套推断方法，包括基于最小二乘的估计、拟合优度检验和显著性检验，并通过模拟和美国、英国收益率曲线数据验证了方法的有效性。
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.