Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
type="main" xml:id="obes12090-abs-0001"> <title type="main">Abstract</title> <p>Bootstrap-based methods for bias-correcting the first-stage parameter estimates used in some recently developed bootstrap implementations of co-integration rank tests are investigated. The procedure constructs estimates of the bias in the original parameter estimates by using the average bias in the corresponding parameter estimates taken across a large number of auxiliary bootstrap replications. A number of possible implementations of this procedure are discussed and concrete recommendations made on the basis of finite sample performance evaluated by Monte Carlo simulation methods. The results show that bootstrap-based bias-correction methods can significantly improve the small sample performance of the bootstrap co-integration rank tests.