2008
DOI: 10.1016/j.econmod.2007.05.003
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Mixed signals among tests for panel cointegration

Abstract: In this paper, we study the effect that different serial correlation adjustment methods can have on panel cointegration testing. As an example, we consider the very popular tests developed by Pedroni (1999Pedroni ( , 2004. Results based on both simulated and real data suggest that different adjustment methods can lead to significant variations in test outcome, and thus also in the conclusions.JEL Classification: C14; C15; C32; C33.

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Cited by 12 publications
(3 citation statements)
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“…However, in spite of their being less general, residual‐based tests turned out to be the most popular in empirical studies. Indeed, as explained by Westerlund and Basher (2008), parametric tests, such as the tests by Larsson et al (2001) and Groen and Kleibergen (2003), that can accommodate quite general dependencies require to empirically determine the appropriate order p for the autoregression, which is typically unknown (and only in this case these tests will not to depend on nuisance parameters). However, if p is underestimated, there will still be a problem of nuisance parameter, whereas if p is overestimated, the small‐sample properties of the test will deteriorate, and this might have a significant impact on cointegration test performance in small samples.…”
Section: Empirical Investigationmentioning
confidence: 99%
“…However, in spite of their being less general, residual‐based tests turned out to be the most popular in empirical studies. Indeed, as explained by Westerlund and Basher (2008), parametric tests, such as the tests by Larsson et al (2001) and Groen and Kleibergen (2003), that can accommodate quite general dependencies require to empirically determine the appropriate order p for the autoregression, which is typically unknown (and only in this case these tests will not to depend on nuisance parameters). However, if p is underestimated, there will still be a problem of nuisance parameter, whereas if p is overestimated, the small‐sample properties of the test will deteriorate, and this might have a significant impact on cointegration test performance in small samples.…”
Section: Empirical Investigationmentioning
confidence: 99%
“…Harb (2004) modeled M1 money demand for GCC countries from 1979-2000 and found cointegration between money and non-oil GDP when the trend is not included. However, using different serial correlation adjustment methods, Westerlund and Basher (2008) could not reject the null hypothesis of no cointegration using the same data as Harb. Further, Harb's use of M1 money is problematic, as it does not accurately reflect the private sector's demand for money in GCC countries (further details provided below).…”
Section: Introductionmentioning
confidence: 99%
“…treatment of serial correlation) used in cointegration testing. For a demonstration of this issue in the context of panel data, see Westerlund and Basher (2008).…”
mentioning
confidence: 99%