This paper studies the detection of step changes in the variances and in the correlation structure of the components of a vector of time series. Two procedures are considered. The first is based on the likelihood ratio test and the second on cusum statistics. These two procedures are compared in a simulation study and we conclude that the cusum procedure is more powerful. The procedures are illustrated in two examples.R

Daniel Peña

Pedro Galeano

English

Research Papers in Economics

A Bayesian analysis of a change in the precision of a sequence of independent normal random variables at an unknown time point”

A Bayesian Approach to retrospective identiﬁcation of the change-points”,

A Multivariate Time Series Analysis of Some Flour Price Data”,

Bayesian approach to inference about a change-point in a sequence of random variables”,

Changes of Variance in First Order Autoregressive Time Series Models - With An Application”,

Detection of Multiple Tests of Variances Using Posterior Odds”,

Identifying a Simplifying Structure in Time Series”,

Inferences About the Parameters of a Time Series Model with Changing Variance”,

On the stable Paretian behavior of stock-market prices”,

Outliers, Level Shifts and Variance Changes in Time Series”,

Speciﬁcation of Echelon-Form VARMA models”,

Testing and Locating Variance Changepoints with Application to Stock Prices”,

Tests for Variance Shift at an Unknown Time Point”,

The Cusum of Squares Test for Scale Changes in Inﬁnite Order Moving Average Models”,

The Cusum of Squares Test for Variance Changes in Inﬁnite Order Autoregressive Models”,

Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance”,

Variance Changes in Autoregressive Models”, Time Series. Theory and Methods, 2nd Ed.

VARIANCE CHANGES DETECTION IN MULTIVARIATE TIME SERIES

http://docubib.uc3m.es/WORKINGPAPERS/WS/ws041305.pdf

VARIANCE CHANGES DETECTION IN MULTIVARIATE TIME SERIES

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics