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Identification robust inference in cointegrating regressions

Khalaf, L. & Urga, G. (2014). Identification robust inference in cointegrating regressions. Journal of Econometrics, 182(2), pp. 385-396. doi: 10.1016/j.jeconom.2014.06.001


In cointegrating regressions, estimators and test statistics are nuisance parameter dependent. This paper addresses this problem from an identification-robust perspective. Confidence sets for the long-run coefficient (denoted β) are proposed that invert LR-tests against an unrestricted or a cointegration-restricted alternative. For empirically relevant special cases, we provide analytical solutions to the inversion problem. A simulation study, imposing and relaxing strong exogeneity, analyzes our methods relative to standard Maximum Likelihood, Fully Modified and Dynamic OLS, and a stationarity-test based counterpart. In contrast with all the above, proposed methods have good size regardless of the identification status, and good power when β is identified.

Publication Type: Article
Additional Information: © 2014, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Publisher Keywords: Cointegration; Weak identification; Bound test; Simulation-based inference
Subjects: H Social Sciences > HG Finance
Departments: Bayes Business School > Finance
Text - Accepted Version
Available under License : See the attached licence file.

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