Stationarity and ergodicity of vector STAR models

05/29/2018
by   Igor L. Kheifets, et al.
0

Smooth transition autoregressive models are widely used to capture nonlinearities in univariate and multivariate time series. Existence of stationary solution is typically assumed, implicitly or explicitly. In this paper we describe conditions for stationarity and ergodicity of vector STAR models. The key condition is that the joint spectral radius of certain matrices is below 1, which is not guaranteed if only separate spectral radii are below 1. Our result allows to use recently introduced toolboxes from computational mathematics to verify the stationarity and ergodicity of vector STAR models.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/21/2022

Margin-closed vector autoregressive time series models

Conditions are obtained for a Gaussian vector autoregressive time series...
research
04/05/2021

Spectral Subsampling MCMC for Stationary Multivariate Time Series

Spectral subsampling MCMC was recently proposed to speed up Markov chain...
research
07/12/2023

Stationarity with Occasionally Binding Constraints

This paper studies a class of multivariate threshold autoregressive mode...
research
01/10/2018

Gaussian Latent Factor Analysis under Graphical Constraints

In this paper we explored the algebraic structure of the solution space ...
research
05/23/2021

Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package

Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics o...
research
01/10/2018

Latent Factor Analysis of Gaussian Distributions under Graphical Constraints

In this paper, we explored the algebraic structures of solution spaces f...
research
11/21/2018

The contribution of star scientists to overall sex differences in research productivity

The state of the art on the issue of sex differences in research efficie...

Please sign up or login with your details

Forgot password? Click here to reset