This thesis consists of three self-contained essays on non-stationary panel data. We propose novel approaches to both cointegration and unit root analysis in panel data models. The main contribution of this thesis is allowing for the presence of cross¬section dependence through the speciﬁcation of an approximate common factor model. Early studies assumed that time series in the panel data were either indepen¬dent or that cross-section dependence could be controlled by including time effects. In macroeconomic, microeconomic and ﬁnancial applications, cross-section depen¬dence is more a recurrent than a rare characteristic and it is usually caused by the presence of common shocks (oil price shocks or ﬁnancial crises) or the existence of local productivity spillover effects. Ignoring these factors can lead to spurious statistical inference.