Nonstationary econometrics with panel data

his dissertation studies the problem of statistical estimation and inference in panel regression models with data for which the time series component is nonstationary (in the form of an integrated or near integrated process) and where both the cross section and the time dimensions are large. Distinguishing effects that nonstationary time series or cross section data alone cannot identify and developing limit theories for multidimensional processes are the two central goals of the theoretical work reported in this dissertation. In addition, the dissertation seeks to illustrate the new methods at work in an empirical application.;The first chapter develops a limit theory that is helpful in understanding and interpreting linear regressions with integrated panel data. We show that there exist interesting long-run average relations between integrated panel vectors when there is no individual time series cointegration and when there is heterogeneous cointegration. The limit theory allows for both sequential limits, wherein...