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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".
We propose a simple modification of the time series filter by Hamilton (2018) that yields reliable and economically meaningful real-time output gap estimates. The original filter relies on 8-quarter-ahead forecast errors of a simple autoregression of log real GDP. While this approach yields a cyclical component of GDP that is hardly revised with new incoming data due to the one-sided filtering approach, it does not cover typical business cycle frequencies evenly, but short business cycles are muted and medium length business cycles are amplified. Further, the estimated trend is as volatile as GDP itself and can thus hardly be interpreted as potential GDP. A simple modification that is based on the mean of 4- to 12-quarter-ahead forecast errors shares the favorable real-time properties of the Hamilton filter, but leads to a much better coverage of typical business cycle frequencies and a smooth estimated trend. Based on output growth and inflation forecasts and a comparison to revised output gap estimates from policy institutions, we find that real-time output gaps based on the modified Hamilton filter are economically much more meaningful measures of the business cycle than those based on other simple statistical trend-cycle decomposition techniques such as the HP or the Bandpass filter.