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

Authors:
Christian Kascha;Carsten Trenkler
Affiliations:
Norges Bank, Research Department, Bankplassen 2, 0107 Oslo, Norway;University of Mannheim, Department of Economics, Chair of Empirical Economics, L7, 3-5, 68131 Mannheim, Germany
Venue:
Computational Statistics & Data Analysis
Year:
2011

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Abstract

The finite-sample size and power properties of bootstrapped likelihood ratio system cointegration tests are investigated via Monte Carlo simulations when the true lag order of the data generating process is unknown. Recursive bootstrap schemes are employed which differ in the way in which the lag order is chosen. The order is estimated by minimizing different information criteria and by combining the corresponding order estimates. It is found that, in comparison to the standard asymptotic likelihood ratio test based on an estimated lag order, bootstrapping can lead to improvements in small samples even when the true lag order is unknown, while the power loss is moderate.