Testing for panel cointegration in an error-correction framework with an application to the Fisher hypothesis

ABSTRACT

In this article, three innovative panel error-correction model (PECM) tests are proposed. These tests are based on the multivariate versions of the Wald (W), likelihood ratio (LR), and Lagrange multiplier (LM) tests. Using Monte Carlo simulations, the size and power of the tests are investigated when the error terms exhibit both cross-sectional dependence and independence. We find that the LM test is the best option when the error terms follow independent white-noise processes. However, in the more empirically relevant case of cross-sectional dependence, we conclude that the W test is the optimal choice. In contrast to previous studies, our method is general and does not rely on the strict assumption that a common factor causes the cross-sectional dependency. In an empirical application, our method is also demonstrated in terms of the Fisher effect—a hypothesis about the existence of which there is still no clear consensus. Based on our sample of the five Nordic countries we utilize our powerful test and discover evidence which, in contrast to most previous research, confirms the Fisher effect.

KEYWORDS:

• Error correction
• Fisher hypothesis
• Multivariate tests
• Panel cointegration
• Size and power

MATHEMATICS SUBJECT CLASSIFICATION:

• C32
• C33

Acknowledgment

All the authors contributed equally.

Notes

1 For an exact definition of the partial Fisher effect, see, for instance Westerlund (2008). For an overview of previous research of the Fisher effect, see, for instance, the review article “The Fisher effect: A review of the literature” by Cooray (2003).