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;
Find related papers by JEL classification:
- 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
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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- Marcus Scheiblecker, 2012. "Between Cointegration and Multicointegration. Modelling Time Series Dynamics by Cumulative Error Correction Models," WIFO Working Papers 431, WIFO.
- Hadri, Kaddour & Kurozumi, Eiji & Rao, Yao, 2013. "Novel Panel Cointegration Tests Emending for Cross-Section Dependence with N Fixed," Discussion Papers 2013-12, Graduate School of Economics, Hitotsubashi University.
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