Dynamic Specification, the Long Run and the Estimation of Transformed Regression Models
Trevor Breusch () and
Michael Wickens
No 154, CEPR Discussion Papers from Centre for Economic Policy Research
Abstract:
This paper discusses the best way to formulate and estimate a dynamic econometric model when interest focuses mainly upon its long-run properties. Using results derived for the more general context of transformed regression models, it is shown how point estimates and the standard errors of long-run multipliers and long-run structural coefficients can be obtained using standard estimation methods. It is argued that such formulations are preferable to other specifications such as the error correction model. If the explanatory variables that enter the long-run solution are trend-stationary then it is found that no harm is done to the asymptotic properties of the long-run coefficients by omitting short-run dynamics entirely, though this is not recommended in practice. The results of this paper are related to the concept of co-integration and to the work of Engle and Granger. Finally, a new methodology for the construction of dynamic models is proposed.
Keywords: Co Integration; Co Integration Theory; Dynamic Specification; Long-Run Models; Non-Stationary Time Series (search for similar items in EconPapers)
Date: 1987-02
References: Add references at CitEc
Citations: View citations in EconPapers (53)
Downloads: (external link)
http://www.cepr.org/active/publications/discussion_papers/dp.php?dpno=154 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:154
Ordering information: This working paper can be ordered from
http://www.cepr.org/ ... pers/dp.php?dpno=154
Access Statistics for this paper
More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().