The Role of "Leads" in the Dynamic OLS Estimation of Cointegrating Regression Models
AbstractIn this paper, we consider the role of "leads" of the first difference of integrated variables in the dynamic OLS estimation of cointegrating regression models. We demonstrate that the role of leads is related to the concept of Granger causality and that in some cases leads are unnecessary in the dynamic OLS estimation of cointegrating regression models. Based on a Monte Carlo simulation, we find that the dynamic OLS estimator without leads substantially outperforms that with leads and lags; we therefore recommend testing for Granger noncausality before estimating models.
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Bibliographic InfoPaper provided by Institute of Economic Research, Hitotsubashi University in its series Hi-Stat Discussion Paper Series with number d06-194.
Date of creation: Dec 2006
Date of revision:
Cointegration; dynamic ordinary least squares estimator; Granger causality;
Other versions of this item:
- Hayakawa, Kazuhiko & Kurozumi, Eiji, 2008. "The role of “leads” in the dynamic OLS estimation of cointegrating regression models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 555-560.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-12-16 (All new papers)
- NEP-ECM-2006-12-16 (Econometrics)
- NEP-ETS-2006-12-16 (Econometric Time Series)
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