Stationary bootstrapping for panel cointegration tests under cross-sectional dependence

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Abstract

Stationary bootstrapping is applied to panel cointegration tests which are based on the ordinary least-squares estimator and the seemingly unrelated regression (SUR) estimator of the residual unit root. Large sample validity of stationary bootstrapping is established. A finite sample experiment reveals that size performances of the bootstrap tests are much less sensitive to cross-sectional correlation than those of existing tests and a test based on the SUR estimator has substantially better power than existing tests.

Original languageEnglish
Pages (from-to)209-223
Number of pages15
JournalStatistics
Volume49
Issue number1
DOIs
StatePublished - 2 Jan 2015

Bibliographical note

Funding Information:
The author is very grateful for an associate editor and a referee whose comments improved the paper considerably. This study was supported by Basic Research Program (2012-001361) and Science Research Center program (2011-0030811) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education Science and Technology.

Publisher Copyright:
© 2014, © 2014 Taylor & Francis.

Keywords

  • cointegration test
  • cross-sectional dependence
  • panel data
  • seemingly unrelated regression estimator
  • stationary bootstrapping

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