Summary This paper develops panel data tests for the null hypothesis of no error correction in a model with common stochastic trends. The asymptotic distributions of the new test statistics are derived and simulation results are provided to suggest that they perform well in small samples. Copyright © 2015 John Wiley & Sons, Ltd.

Panel 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, -91] via Monte Carlo simulations. Several studies have recently addressed this issue for panel unitroot tests using a common factor structure to model the cross-sectional dependence, but not much work has been done yet for panel nocointegration tests. This paper proposes a model for panel no-cointegration using an unobserved common factor structure, following the study by Bai 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 *Previous versions of this paper were presented at ]. Under the data-generating processes (DGP) used, the test statistics are no longer asymptotically normal, and convergence occurs at rate T rather than ffiffiffiffi N p T 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.

Several panel unit root tests that account for cross-section dependence using a common factor structure have been proposed in the literature recently. Pesaran's (2007) cross-sectionally augmented unit root tests are designed for cases where cross-sectional dependence is due to a single factor. The Moon and Perron (2004) tests which use defactored data are similar in spirit but can account for multiple common factors. The Bai and Ng (2004a) tests allow to determine the source of nonstationarity by testing for unit roots in the common factors and the idiosyncratic factors separately. Breitung and Das (2008) and Sul (2007) propose panel unit root tests when cross-section dependence is present possibly due to common factors, but the common factor structure is not fully exploited. This article makes four contributions: (1) it compares the testing procedures in terms of similarities and differences in the data generation process, tests, null, and alternative hypotheses considered, (2) using Monte Carlo results it compares the small sample properties of the tests in models with up to two common factors, (3) it provides an application which illustrates the use of the tests, and (4) finally, it discusses the use of the tests in modelling in general.Cross-section dependence, Factor models, Non-stationary panel data, Unit root tests, C32, C33,

The aim of this paper is to investigate the long run relationship between the development of banks and stock markets and economic growth. We make use of the Groen and Kleibergen (2003) panel cointegration methodology to test the number of cointegrating vectors among these three variables for 5 developing countries. In addition, we test the direction of potential causality between financial and economic development. Our results conclude to the existence of a single cointegrating vector between financial development and growth and of causality going from financial development to economic growth. We find little evidence of reverse causation as well as bi-directional causality.

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People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.Working on my Ph.D. over the past years has been an exciting experience. There has been the joy when interesting results had been obtained after some hard work and when they were accepted for presentation or publication.. But there has also been frustration when results turned out uninteresting or no results could been obtained at all, and I realized that I had wasted time and energy running around in circles. However, that is also part of research, and looking back I have to say that the positive moments by far outnumber the negative ones.I would like to take the opportunity here to thank a number of people whose continued support helped me a lot over the recent years. First and foremost I would like to thank my supervisors, Franz Palm and Jean-Pierre Urbain. I have enjoyed our collaboration tremendously over the years, not only during our stimulating and at times even merry meetings. I am also grateful for the fact that both your doors are always open to discuss questions, problems or ideas. Furthermore, I would like to mention that Jean-Pierre not only has to accept main responsibility for me pursuing a Ph.D., since he contacted me about that possibility towards the end of my master, but his teaching was influential in my choice of specializing in econometrics in the first place.I would like to thank the members of the reading committee, Martin Carree, Jörg Breitung and Pierre Mohnen, for their time and careful reading of the manuscript. Jörg also deserves thanks for comments and discussion at earlier stages of the work. I thank all my colleagues at the Department of Quantitative Economics for providing an extremely pleasant working atmosphere. In particular, I would like to thank Karin van den Boorn and Haydeé Hallmanns for their administrative support and Stephan Smeekes, who personally contributed to this thesis in form of the Dutch summary. I am also grateful to all colleagues I met at conferences and workshops and who provided stimulating discussion and comments. Especially, I would like to mention Joakim Westerlund, who is mor...