The paper reports simulation and empirical evidence on the finite-sample performance of adaptive estimators in cointegrated systems. Adaptive estimators are asymptotically efficient, even when the shape of the likelihood function is unknown. We consider two representations of cointegrated systems-triangular cointegrating regressions and error correction models. The motivation for and advantages of adaptive estimators in such systems are discussed and their construction is described. We report results from the estimation of a forward exchange market unbiasedness regression using the adaptive and competing estimators, and provide related Monte Carlo simulation evidence on the performance of the estimators.