Approximate Functional Factor Models

Abstract
We propose an approximate factor model for time-dependent curve data that represents a functional time series as the aggregate of a predictive low-dimensional component and an unpredictive infinite-dimensional component.

Suitable identification conditions lead to a two-stage estimation procedure based on functional principal components, and the number of factors is estimated consistently through an information criterion-based approach. The methodology is applied to the problem of modeling and predicting yield curves. Our results indicate that more than three factors are required to characterize the dynamics of the term structure of bond yields.

(This is joint work with Sven Otto.)

About Nazarii Salish
Nazarii Salish is an Associate Professor of Economics at the University Carlos III de Madrid. He has been working at the University since 2016, first as an Assistant Professor. Nazarii holds a PhD in Economics from the Bonn Graduate School of Economics (BGSE) at the University of Bonn in Germany. His research interests include Functional Data Analysis, Panel Data Models, Nonlinear Models, Time Series Econometrics, and Environmental Economics. He has published several articles in reputable academic journals, including the Journal of Econometrics and the Econometrics Journal.