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Empirical evidence has shown that a prerequisite for generating reliable macroeconomic forecasts is either the inclusion of a large information set or modeling time variation in the models’ parameters and volatilities. In this paper we examine these claims in a comparative manner, forecasting GDP growth for six CESEE economies. We use Bayesian techniques and evaluate the models based on both the accuracy of their point forecasts as well as the degree of uncertainty surrounding these predictions. Our results indicate that forecasts from a fully-fledged time-varying parameter model tend to outperform those from its constant parameter competitors. Adding more information, e.g. from other countries, by contrast, does not improve forecast performance significantly for most of the countries under study. Last, we analyze whether it pays to forecast GDP growth indirectly by summing up forecasts of GDP components. This approach yields competitive forecasts, yet it preserves an economic interpretation of the underlying drivers for the economic growth forecasts, which is of crucial importance from a practitioner’s view.