DeepAI AI Chat
Log In Sign Up

Threshold factor models for high-dimensional time series

by   Xialu Liu, et al.
San Diego State University
Rutgers University

We consider a threshold factor model for high-dimensional time series in which the dynamics of the time series is assumed to switch between different regimes according to the value of a threshold variable. This is an extension of threshold modeling to a high-dimensional time series setting under a factor structure. Specifically, within each threshold regime, the time series is assumed to follow a factor model. The factor loading matrices are different in different regimes. The model can also be viewed as an extension of the traditional factor models for time series. It provides flexibility in dealing with situations that the underlying states may be changing over time, as often observed in economic time series and other applications. We develop the procedures for the estimation of the loading spaces, the number of factors and the threshold value, as well as the identification of the threshold variable. The theoretical properties are investigated. Simulated and real data examples are presented to illustrate the performance of the proposed method.


Helping Effects Against Curse of Dimensionality in Threshold Factor Models for Matrix Time Series

As is known, factor analysis is a popular method to reduce dimension for...

Factor Modelling for Clustering High-dimensional Time Series

We propose a new unsupervised learning method for clustering a large num...

Correlation networks, dynamic factor models and community detection

A dynamic factor model with a mixture distribution of the loadings is in...

Deep Dynamic Factor Models

We propose a novel deep neural net framework - that we refer to as Deep ...

Mixed Membership Models for Time Series

In this article we discuss some of the consequences of the mixed members...

Modeling Regime Shifts in Multiple Time Series

We investigate the problem of discovering and modeling regime shifts in ...