Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-22T11:17:57.226Z Has data issue: false hasContentIssue false

12 - Unit root tests for time series with a structural break when the break point is known

Published online by Cambridge University Press:  05 June 2012

Helmut Lütkepohl
Affiliation:
Humboldt University
Christian Müller
Affiliation:
Humboldt University
Pentti Saikkone
Affiliation:
University of Helsinki
Cheng Hsiao
Affiliation:
University of Southern California
Kimio Morimune
Affiliation:
Kyoto University, Japan
James L. Powell
Affiliation:
University of California, Berkeley
Get access

Summary

Introduction

A number of studies consider testing for unit roots in univariate time series which have a level shift. Examples are Perron (1989, 1990), Perron and Vogelsang (1992), Banerjee, Lumsdaine, and Stock (1992), Zivot and Andrews (1992), Amsler and Lee (1995), Leybourne, Newbold, and Vougas (1998), Montanes and Reyes (1998), and Saikkonen and Lütkepohl (1999). These tests are important because the trending properties of a set of time series determine to some extent which model and statistical procedures are suitable for analyzing their relationship. In the aforementioned studies different models and assumptions for the structural shift are considered. In some of the studies the timing of the break point is assumed to be known, whereas in others a shift in an unknown period is considered. There seems to be general consensus, however, that if the break point is known, this is useful information which should be taken into account in the subsequent analysis and in particular in testing for unit roots. Therefore we will focus on the latter case in the following. In practice, a known break point is quite common. For instance, many German macroeconomic time series are known to have a shift in 1990 where the German reunification took place.

For the case of a known break point we will propose a framework which generalizes previously considered models. In this framework the shift is modeled as part of the intercept term of the stationary part of the data generation process (DGP) which is clearly separated from the unit root part.

Type
Chapter
Information
Nonlinear Statistical Modeling
Proceedings of the Thirteenth International Symposium in Economic Theory and Econometrics: Essays in Honor of Takeshi Amemiya
, pp. 327 - 348
Publisher: Cambridge University Press
Print publication year: 2001

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×