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Efficient Tests for General Persistent Time Variation in Regression Coefficients1

Efficient Tests for General Persistent Time Variation in Regression Coefficients1 There are a large number of tests for instability or breaks in coefficients in regression models designed for different possible departures from the stable model. We make two contributions to this literature. First, we consider a large class of persistent breaking processes that lead to asymptotically equivalent efficient tests. Our class allows for many or relatively few breaks, clustered breaks, regularly occurring breaks, or smooth transitions to changes in the regression coefficients. Thus, asymptotically nothing is gained by knowing the exact breaking process of the class. Second, we provide a test statistic that is simple to compute, avoids any need for searching over high dimensions when there are many breaks, is valid for a wide range of data-generating processes and has good power and size properties even in heteroscedastic models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Review of Economic Studies Oxford University Press

Efficient Tests for General Persistent Time Variation in Regression Coefficients1

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References (58)

Publisher
Oxford University Press
Copyright
© Published by Oxford University Press.
Subject
Original Articles
ISSN
0034-6527
eISSN
1467-937X
DOI
10.1111/j.1467-937X.2006.00402.x
Publisher site
See Article on Publisher Site

Abstract

There are a large number of tests for instability or breaks in coefficients in regression models designed for different possible departures from the stable model. We make two contributions to this literature. First, we consider a large class of persistent breaking processes that lead to asymptotically equivalent efficient tests. Our class allows for many or relatively few breaks, clustered breaks, regularly occurring breaks, or smooth transitions to changes in the regression coefficients. Thus, asymptotically nothing is gained by knowing the exact breaking process of the class. Second, we provide a test statistic that is simple to compute, avoids any need for searching over high dimensions when there are many breaks, is valid for a wide range of data-generating processes and has good power and size properties even in heteroscedastic models.

Journal

The Review of Economic StudiesOxford University Press

Published: Oct 1, 2006

Keywords: JEL classification C22

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