Abstract :
Saikkonen ~1991, Econometric Theory 7, 1–21! developed an asymptotic optimality
theory for the estimation of cointegrated regressions+ He proposed the dynamic
ordinary least squares ~OLS! estimator obtained by augmenting the static cointegrating
regression with leads and lags of the first differences of the I~1! regressors+
However, the assumptions imposed preclude the use of information criteria
such as the Akaike information criterion ~AIC! and Bayesian information criterion
~BIC! to select the number of leads and lags+ We show that his results remain
valid under weaker conditions that permit the use of such data dependent rules+
Simulations show that, relative to sequential general to specific testing procedures,
the use of such information criteria can indeed produce estimates with
smaller mean squared errors and confidence intervals with better coverage rates+
