The role of “leads" in the dynamic OLS estimation of cointegrating regression models
Mathematics and Computers in Simulation Volume 79 Issue 3
Page 555-560
published_at 2008-12
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| File | |
| Title ( eng ) |
The role of “leads" in the dynamic OLS estimation of cointegrating regression models
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| Creator |
Kurozumi Eiji
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| Source Title |
Mathematics and Computers in Simulation
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| Volume | 79 |
| Issue | 3 |
| Start Page | 555 |
| End Page | 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.
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| Keywords |
cointegration
dynamic ordinary least squares estimator
granger causality
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| NDC |
Economics [ 330 ]
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| Language |
eng
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| Resource Type | journal article |
| Publisher |
Elsevier Ltd
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| Date of Issued | 2008-12 |
| Rights |
Copyright (c) 2008 IMACS Published by Elsevier Ltd.
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| Publish Type | Author’s Original |
| Access Rights | open access |
| Source Identifier |
[ISSN] 0378-4754
[DOI] 10.1016/j.matcom.2008.02.027
[NCID] AA00723761
[DOI] http://dx.doi.org/10.1016/j.matcom.2008.02.027
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