This paper proposes a test statistic for the null hypothesis of panel stationarity that allows for the presence of multiple structural breaks. Two different specications are considered depending on the structural breaks affecting the individual effects and/or the time trend. The model is exible enough to allow the number of breaks and their position to differ across individuals. The test is shown to have an exact limit distribution with a good nite sample performance. Its application to a typical panel data set of real per capita GDP gives support to the trend stationarity of these series.Keywords: multiple structural changes, panel data, stationarity test, GDP per capita JEL codes: C12, C22 ResumAquest article proposa un estadístic de prova per contrastar la hipòtesi nul¢la d'estacionarietat en panell permetent la presència de múltiples canvis estructurals. Es consideren dues especicacions diferents en funció de si els canvis estructurals afecten els efectes individuals i/o la tendència temporal. El model és el sucientment exible com per permetre que tant el nombre de canvis com la seva posició puguin diferir entre els individus. El treball mostra que la distribució asimptòtica de l'estadístic és exacta. Experiments de simulació indiquen que el comportament del contrast en mides mostrals nites és bo. La seva aplicació a un panell típic de PIB per capita real proporciona evidència a favor de l'estacionarietat de les sèries.

Perron (1989, Econometrica 57, 1361–1401) introduced unit root tests valid when a break at a known date in the trend function of a time series is present. In particular, they allow a break under both the null and alternative hypotheses and are invariant to the magnitude of the shift in level and/or slope. The subsequent literature devised procedures valid in the case of an unknown break date. However, in doing so most research, in particular the commonly used test of Zivot and Andrews (1992, Journal of Business & Economic Statistics 10, 251–270), assumed that if a break occurs it does so only under the alternative hypothesis of stationarity. This is undesirable for several reasons. Kim and Perron (2009, Journal of Econometrics 148, 1–13) developed a methodology that allows a break at an unknown time under both the null and alternative hypotheses. When a break is present, the limit distribution of the test is the same as in the case of a known break date, allowing increased power while maintaining the correct size. We extend their work in several directions: (1) we allow for an arbitrary number of changes in both the level and slope of the trend function; (2) we adopt the quasi–generalized least squares detrending method advocated by Elliott, Rothenberg, and Stock (1996, Econometrica 64, 813–836) that permits tests that have local asymptotic power functions close to the local asymptotic Gaussian power envelope; (3) we consider a variety of tests, in particular the class of M-tests introduced in Stock (1999, Cointegration, Causality, and Forecasting: A Festschrift for Clive W.J. Granger) and analyzed in Ng and Perron (2001, Econometrica 69, 1519–1554).

This paper studies the problem of unit root testing in the presence of multiple structural changes and common dynamic factors. Structural breaks represent infrequent regime shifts, while dynamic factors capture common shocks underlying the comovement of economic time series. We examine the modified Sargan-Bhargava (MSB) test in the panel data setting and propose ways to handle multiple structural changes and dynamic factors. Properties of the MSB test under these non-standard conditions are derived. For example, the test statistics are shown to be invariant, in the limit, to mean breaks. This invariance does not carry over to breaks in linear trends, where the test statistics will converge to functionals of weighted Brownian bridges. A simplified test statistic is then proposed, which is invariant to both mean and trend breaks. We further study pooled test statistic based on standardization and combination of "p"-values. Response surfaces for "p"-values of all test statistics are computed to facilitate the empirical implementation of the proposed methodology. The pooled tests are shown to have good finite sample performance. Copyright © 2009 The Review of Economic Studies Limited.

The power of standard panel cointegration statistics may be affected by misspecification errors if structural breaks in the parameters generating the process are not considered. In addition, the presence of cross-section dependence among the panel units can distort the empirical size of the statistics. We therefore design a testing procedure that allows for both structural breaks and cross-section dependence when testing the null hypothesis of no cointegration. The paper proposes test statistics that can be used when one or both features are present. We illustrate our proposal by analysing the pass-through of import prices on a sample of European countries.