This paper presents Monte Carlo simulations which compare the empirical performance of two alternative single equation estimators of the equilibrium parameters in a dynamic relationship. The estimators considered are Stock and Watson’s dynamic ordinary least squares (DOLS) estimator and Bewley’s transformation of the general autoregressive distributed lag model. The results indicate that the Bewley transformation produces a lower mean-square error as well as superior serial correlation properties even with lower truncation lags for the lagged variables included in the estimation equation. An application is then provided which examines the nature of the equilibrium relationship between aggregate US exports, world trade and the US real exchange rate. This confirms that estimation of the equilibrium parameters of this relationship by the Bewley transformation produces results which are superior to estimation by DOLS.