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Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order

Authors
Disciplines
  • Mathematics

Abstract

We investigate the small-sample size and power properties of bootstrapped likelihood ratio systems cointegration tests via Monte Carlo simulations when the true lag order of the data generating process is unknown. A recursive bootstrap scheme is employed. We estimate the order by minimizing different information criteria. In comparison to the standard asymptotic likelihood ratio test based on an estimated lag order we found that the recursive bootstrap procedure can lead to improvements in small samples even when the true lag order is unknown while the power loss is moderate.

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