Minimum reversion in multivariate time series

11/26/2018
by   Torsten Kleinow, et al.
0

We propose a new multivariate time series model in which we assume that each component has a tendency to revert to the minimum of all components. Such a specification is useful to describe phenomena where each member in a population which is subjected to random noise mimics the behaviour of the best performing member. We show that the proposed dynamics generate co-integrated processes.We characterize the model's asymptotic properties for the case of two populations and show a stabilizing effect on long term dynamics in simulation studies. An empirical study involving human survival data in different countries provides an example which confirms the occurrence of the phenomenon of reversion to the minimum in real data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/10/2018

Multivariate Bayesian Structural Time Series Model

This paper deals with inference and prediction for multiple correlated t...
research
01/14/2018

On the number of signals in multivariate time series

We assume a second-order source separation model where the observed mult...
research
07/03/2018

Time Series Modeling on Dynamic Networks

We consider multivariate time series on dynamic networks with a fixed nu...
research
09/17/2022

Neighborhood VAR: Efficient estimation of multivariate timeseries with neighborhood information

In data science, vector autoregression (VAR) models are popular in model...
research
12/19/2021

Temporal and spectral governing dynamics of Australian hydrological streamflow time series

We use new and established methodologies in multivariate time series ana...
research
06/14/2023

Phase Transitions of Civil Unrest across Countries and Time

Phase transitions, characterized by abrupt shifts between macroscopic pa...
research
05/19/2020

Multiscale modelling of replicated nonstationary time series

Within the neurosciences, to observe variability across time in the dyna...

Please sign up or login with your details

Forgot password? Click here to reset