Estimation and inference with non-stationary panel time-series data

The econometric theory for panel data regressions was largely developed for survey data where N the number of individuals was large and T the number of time periods small. The asymptotic statistical theory was derived by letting N → ∞ for fixed T . In recent years there has been growing interest in cases, such as sets of countries, regions or industries, where there are fairly long time-series for a large number of groups. These large T , large N , panel time-series raise a number of issues. First, since time-series tend to be non-stationary, determining the order of integration and cointegration becomes important. Second, since it is possible to estimate a separate regression for each group, it is natural to think of heterogeneous panels where parameters differ over groups. One can then test for parameter homogeneity rather than having to assume it as one is forced to do in small T panels. Third, one needs to determine the asymptotic properties of standard panel estimators when the data are non-stationary. Fourth, there is an issue of how to do the asymptotic analysis as both N and T can go to infinity. These issues have generated a large amount of recent econometric work and this paper provides an introductory survey for applied workers. Acknowledgement 1 I am grateful to the ESRC for support under grant L138251003. Prepared for the RC33 Conference, Cologne Octo-