This paper studies the smooth transition regression model where regressors are I(1) and errors are I(0). The regressors and errors are assumed to be dependent both serially and contemporaneously. Using the triangular array asymptotics, the nonlinear least squares estimator is shown to be consistent and its asymptotic distribution is derived. It is found that the asymptotic distribution involves a bias under the regressor-error dependence, which implies that the non-linear least squares estimator is inefficient and unsuitable for use in hypothesis testing. Thus, this paper proposes a Gauss-Newton type estimator which uses the NLLS estimator as an initial estimator and is based on regressions augmented by leads-and-lags. Using leads-and-lags enables the Gauss-Newton estimator to eliminate the bias and have a mixture normal distribution in the limit, which makes it efficient and suitable for use in hypothesis testing. Simulation results indicate that the results obtained from the triangular array asymptotics provide reasonable approximations for the þnite sample properties of the estimators and t-tests when sample sizes are moderately large. The cointegrating smooth transition regression model is applied to the Korean and Indonesian data from the Asian currency crisis of 1997. Signiþcant nonlinear effects of interest rate on spot exchange rate are found to be present in the Korean data, which partially supports the interest Laffer curve hypothesis. But overall the effects of interest rate on spot exchange rate are shown to be quite negligible in both the nations.