Nonparametric Estimation of Functional Dynamic Factor Model

TL;DR

This paper introduces nonparametric methods for estimating functional dynamic factor models that account for time dependence, demonstrating improved accuracy over traditional principal component approaches through simulations and real data application.

Contribution

It develops novel nonparametric estimators for functional factor models that incorporate time dependence, applicable to both stationary and nonstationary processes.

Findings

Estimators are consistent under various conditions.

Method outperforms functional principal component estimators in simulations.

Application to yield curves shows practical effectiveness.

Abstract

Data can be assumed to be continuous functions defined on an infinite-dimensional space for many phenomena. However, the infinite-dimensional data might be driven by a small number of latent variables. Hence, factor models are relevant for functional data. In this paper, we study functional factor models for time-dependent functional data. We propose nonparametric estimators under stationary and nonstationary processes. We obtain estimators that consider the time-dependence property. Specifically, we use the information contained in the covariances at different lags. We show that the proposed estimators are consistent. Through Monte Carlo simulations, we find that our methodology outperforms estimators based on functional principal components. We also apply our methodology to monthly yield curves. In general, the suitable integration of time-dependent information improves the estimation of the latent factors.