Testing for Panel Cointegration in an Error Correction Framework - with an Application to the Fisher Hypothesis

In this paper, 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. By means of Monte Carlo simulations, the size and power properties 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, regarding 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 as regards to the Fisher effect – a hypothesis about whose existence is still no clear consensus. Based on a sample of the Nordic countries, we utilize our powerful test and discover evidence which, in contrast to most previous research, confirms the Fisher effect.