Publication Type

Journal Article

Publication Date

1-2014

Abstract

It has been known since Phillips and Hansen (1990) that cointegrated systems can be consistently estimated using stochastic trend instruments that are independent of the system variables. A similar phenomenon occurs with deterministically trending instruments. The present work shows that such "irrelevant" deterministic trend instruments may be systematically used to produce asymptotically efficient estimates of a cointegrated system. The approach is convenient in practice, involves only linear instrumental variables estimation, and is a straightforward one step procedure with no loss of degrees of freedom in estimation. Simulations reveal that the procedure works well in practice both in terms of point and interval estimation, having little finite sample bias and less finite sample dispersion than other popular cointegrating regression procedures such as reduced rank VAR regression, fully modified least squares, and dynamic OLS. The procedure is a form of maximum likelihood estimation where the likelihood is constructed for data projected onto the trending instruments. This "trend likelihood" is related to the notion of the local Whittle likelihood but avoids frequency domain issues. (C) 2013 Elsevier B.V. All rights reserved.

Keywords

Asymptotic efficiency, Cointegrated system, Coverage probability, Instrumental variables, Irrelevant instrument, Karhunen-Loeve representation, Optimal estimation, Orthonormal basis, Sieve estimation of stochastic processes, Trend basis, Trend likelihood

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

178

First Page

210

Last Page

224

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2013.08.022

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1016/j.jeconom.2013.08.022

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Econometrics Commons

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