This paper presents a structural change test for panel data models in which the break (or the change) affects some, but not all, cross-section units in the panel. The test is robust to non-normal, heteroskedastic and autocorrelated errors, as well as end-of-sample structural change. The test amounts to computing and comparing pre- and post-break sample statistics as Chow (1960) type F statistics averaged over cross-section units. The cases of known and unknown break date are both considered. Under mild assumptions, the test has a limiting standard normal distribution as the number of cross-sections tends to infinity. Monte Carlo experiments show that the test has good size and power under a wide range of circumstances, including when the break date is unknown and differs across individual units, and when errors exhibit cross-section dependence. Finally, the test is illustrated by seeking a break in the dynamics of trade among euro area countries following the introduction of the euro.
The Journal of Time Series Econometrics (JTSE) serves as an internationally recognized outlet for important new research in both theoretical and applied classical and Bayesian time series, spatial and panel data econometrics. The scope of the journal includes papers dealing with estimation, testing and other methodological aspects involved in the application of time series and spatial analytic techniques to economic, financial and related data.