Identification robust inference in cointegrating regressions

Lynda Khalaf and Giovanni Urga

Journal of Econometrics, 2014, vol. 182, issue 2, 385-396

Abstract: 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.

Keywords: Cointegration; Weak identification; Bound test; Simulation-based inference (search for similar items in EconPapers)
JEL-codes: C12 C32 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:182:y:2014:i:2:p:385-396

DOI: 10.1016/j.jeconom.2014.06.001

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