Cointegration Testing in Panels with Common Factors
AbstractPanel unit-root and no-cointegration tests that rely on cross-sectional independence of the panel unit experience severe size distortions when this assumption is violated, as has, for example, been shown by Banerjee, Marcellino and Osbat ["Econometrics Journal" (2004), Vol. 7, pp. 322-340; "Empirical Economics" (2005), Vol. 30, pp. 77-91] via Monte Carlo simulations. Several studies have recently addressed this issue for panel unit-root tests using a common factor structure to model the cross-sectional dependence, but not much work has been done yet for panel no-cointegration tests. This paper proposes a model for panel no-cointegration using an unobserved common factor structure, following the study by Bai and Ng ["Econometrica" (2004), Vol. 72, pp. 1127-1177] for panel unit roots. We distinguish two important cases: (i) the case when the non-stationarity in the data is driven by a reduced number of common stochastic trends, and (ii) the case where we have common and idiosyncratic stochastic trends present in the data. We discuss the homogeneity restrictions on the cointegrating vectors resulting from the presence of common factor cointegration. Furthermore, we study the asymptotic behaviour of some existing residual-based panel no-cointegration tests, as suggested by Kao ["Journal of Econometrics" (1999), Vol. 90, pp. 1-44] and Pedroni ["Econometric Theory" (2004a), Vol. 20, pp. 597-625]. Under the data-generating processes (DGP) used, the test statistics are no longer asymptotically normal, and convergence occurs at rate "T" rather than as for independent panels. We then examine the possibilities of testing for various forms of no-cointegration by extracting the common factors and individual components from the observed data directly and then testing for no-cointegration using residual-based panel tests applied to the defactored data. Copyright 2006 Blackwell Publishing Ltd.
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Bibliographic InfoArticle provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.
Volume (Year): 68 (2006)
Issue (Month): s1 (December)
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