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Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data$
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Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data

Anindya Banerjee, Juan J. Dolado, John W. Galbraith, and David Hendry

Print publication date: 1993

Print ISBN-13: 9780198288107

Published to Oxford Scholarship Online: November 2003

DOI: 10.1093/0198288107.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use.date: 02 July 2022

Co‐Integration in Individual Equations

Co‐Integration in Individual Equations

Chapter:
(p.204) 7 Co‐Integration in Individual Equations
Source:
Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data
Author(s):

Anindya Banerjee (Contributor Webpage)

Juan J. Dolado

John W. Galbraith (Contributor Webpage)

David F. Hendry (Contributor Webpage)

Publisher:
Oxford University Press
DOI:10.1093/0198288107.003.0007

Examines methods of testing for co‐integration in single equations via static regressions, and provides simulation estimates of the percentiles of the distributions of statistics used in these tests. The finite‐sample biases of the estimates of the co‐integrating vectors and powers of the tests based on static regressions are discussed within the framework of extensive Monte Carlo simulations. Dynamic models leading to an error‐correction mechanism based test (ECM test for co‐integration) and non‐parametrically modified estimators are also considered as better ways of estimating the co‐integrating relationships.

Keywords:   biases, co‐integration tests, dynamic specification, ECM test for co‐integration, fully modified estimation, Monte Carlo simulation, response surfaces, static regression

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