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This thesis is concerned with the derivation of new methods for the analysis of nonstationary, cross correlated panels. The suggested procedures are carefully quantified by means of Monte Carlo experiments. Typical applications of the developed methods consist in multi-country studies, with several countries observed over a couple of decades. The empirical applications implemented here are the testing for trends in the investment share in European GDPs and the examination of OECD interest rates. In the first chapter, a panel test for the presence of a linear time trend is proposed. The test is applicable in cross-correlated, heterogeneous panels and it can also be used when the integration order of innovations is unknown, by means of subsampling. In the next chapter a cointegration test having asymptotic standard normal distributiun and not requiring exogeneity assumptions is derived. In panels exhibiting cross-correlation or cointegration, individual test statistics are asymptotically independent, which leads to a panel test statistic robust to dependence across units. The third chapter examines in an econometric context the simple idea of combining p-values from a series of statistical tests and improves its applicability in the presence of cross-correlation. The last chapter applies recent panel techniques to OECD long-term interest rates and differentials thereof, finding only rather week evidence in favor of stationarity when allowing for cross-correlation.
This thesis is concerned with various aspects of estimating trend output and growth and discusses and evaluates methods to prepare medium-term GDP growth projections. Furthermore, econometric techniques suited for cross-correlated macroeconomic panel data with a focus on factor models are applied for unit root and cointegration testing as well as panel error correction estimation. Applications involve the identification of growth determinants as well as the modelling of aggregate labor supply in a multi-country framework. The first chapter evaluates a very popular method for potential output estimation and medium-term forecasting---the production function approach---in terms of predictive performance. For this purpose, a particular forecast evaluation framework is developed and an evaluation of the predictions of GDP growth for the three to five years ahead for each individual G7 country is carried out. In chapter two, a new approach for estimating trend growth of advanced economies is proposed. The suggestion combines econometric methods that have been used to test and estimate the implications of the extended Solow growth model in a cross sectional time series setting with an application of multivariate time series filter techniques. The last chapter discusses several panel unit root tests designed to accommodate cross-sectional dependence. These methods are then applied to an OECD country sample of the aggregate labor supply measure "hours worked".