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This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T) and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components.

This book provides an introduction to the technical background of unit root testing, one of the most heavily researched areas in econometrics over the last twenty years. Starting from an elementary understanding of probability and time series, it develops the key concepts necessary to understand the structure of random walks and Brownian motion, and their role in tests for a unit root. The techniques are illustrated with worked examples, data and programs available on the book's website, which includes more numerical and theoretical examples. This book is indispensable reading for all interested in Time Series Econometrics, Econometrics and Applied Econometrics.

This paper proposes the use of an improved covariate unit root test which exploits the cross‐sectional dependence information when the panel data null hypothesis of a unit root is rejected. More explicitly, to increase the power of the test, we suggest the utilization of more than one covariate and offer several ways to select the 'best' covariates from the set of potential covariates represented by the individuals in the panel. Employing our methods, we investigate the Prebish‐Singer hypothesis for nine commodity prices. Our results show that this hypothesis holds for all but the price of petroleum.