The role of “leads" in the dynamic OLS estimation of cointegrating regression models
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dynamic ordinary least squares estimator
In this paper, we consider the role of “leads" of the first difference of integrated variables in the dynamic OLS estimation of cointegrating regression models. Specifically, we investigate Stock and Watson's [J.H. Stock, M.W. Watson's, A simple estimator of cointegrating vectors in higher order integrated systems, Econometrica 61 (1993) 783–820] claim that the role of leads is related to the concept of Granger causality by a Monte Carlo simulation. From the simulation results, we find that the dynamic OLS estimator without leads substantially outperforms that with leads and lags; we therefore recommend testing for Granger non-causality before estimating models.
Mathematics and Computers in Simulation
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Copyright (c) 2008 IMACS Published by Elsevier Ltd.
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Graduate School of Social Sciences
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