The role of “leads” in the dynamic OLS estimation of cointegrating regression models

Kazuhiko Hayakawa and Eiji Kurozumi ()

Mathematics and Computers in Simulation (MATCOM), 2008, vol. 79, issue 3, 555-560

Abstract: 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.

Keywords: Cointegration; Dynamic ordinary least squares estimator; Granger causality (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Related works:
Working Paper: The Role of "Leads" in the Dynamic OLS Estimation of Cointegrating Regression Models (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:79:y:2008:i:3:p:555-560

DOI: 10.1016/j.matcom.2008.02.027

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