Twentieth century shocks, trends and cycles in industrialized nations

Using annual data on real Gross Domestic Product per capita of seventeen industrialized nations in the twentieth century the empirical relevance of shocks, trends and cycles is investigated. A class of neural network models is specified as an extension of the class of vector autoregressive models in order to capture complex data patterns for different countries and subperiods. Empirical evidence indicates nonlinear positive trends in the levels of real GDP per capita, time varying growth rates, switching behavior of individual countries with respect to their position in the distribution of real GDP per capita levels over time and club behavior with respect to convergence. Such evidence presents great challenges for economic modelling, forecasting and policy analysis in the long run.