Assessing Persistence in Discrete Nonstationary Time-Series Models

13 Pages Posted: 18 Feb 2005

See all articles by Brendan P.M. McCabe

Brendan P.M. McCabe

University of Liverpool - Management School (ULMS)

G. M. Martin

Monash University

Andy Tremayne

The University of Sydney - Discipline of Econometrics and Business Statistics

Abstract

The aim of this paper is to examine the application of measures of persistence in a range of time-series models nested in the framework of Cramer (1961). This framework is a generalization of the Wold (1938) decomposition for stationary time-series which, in addition to accommodating the standard I(0) and I(1) models, caters for a broad range of alternative processes. Two measures of persistence are considered in some detail, namely the long-run impulse-response and variance-ratio functions. Particular emphasis is given to the behaviour of these measures in a range of non-stationary models specified in discrete time. We document the conflict that arises between different measures, applied to the same model, as well as conflict arising from the use of a given measure in different models. Precisely which persistence measures are time dependent and which are not, is highlighted. The nature of the general representation used also helps to clarify which shock the impulse-response function refers to in the case of models where more than one random disturbance impinges on the time series.

Keywords: Cramer representation, stochastic unit root model, stochastic integration, impulse response, variance ratio

JEL Classification: C10, C22

Suggested Citation

McCabe, Brendan P.M. and Martin, G. M. and Tremayne, Andrew Ronald, Assessing Persistence in Discrete Nonstationary Time-Series Models. Available at SSRN: https://ssrn.com/abstract=668626

Brendan P.M. McCabe (Contact Author)

University of Liverpool - Management School (ULMS) ( email )

Chatham Street
Liverpool, L69 7ZH
United Kingdom

G. M. Martin

Monash University

23 Innovation Walk
Wellington Road
Clayton, Victoria 3800
Australia

Andrew Ronald Tremayne

The University of Sydney - Discipline of Econometrics and Business Statistics ( email )

Room No 482
Sydney NSW 2006
Australia
+61 2 9351 2787 (Phone)
+61 2 9351 6409 (Fax)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
12
Abstract Views
1,215
PlumX Metrics