Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data

The main goal of the article is to investigate forecasting quality of two approaches to modelling main macroeconomic variables without a priori assumptions concerning causality and generate forecasts without additional assumptions regarding regressors. With application of tendency survey data the authors develop methodology for application of the Bayesian averaging of classical estimates (BACE) but also construct dynamic factor models (DFM). Within the BACE framework they apply two diversified methods of regressors' selection: frequentist (FMA) and averaging (BMA). Because their models yield multiple forecasts for each period, subsequently the authors employ diversified approaches to combine forecasts. The assessment of the results is performed with in-sample and out-of-sample prediction errors. Although the results do not significantly differ, the best performance is observed in Bayesian models with frequentist approach. Their analysis conducted for Polish economy also shows that the unemployment rate turns out to be forecasted with highest precision, followed by the rate of GDP growth and the CPI. It can be concluded from their analyses that although their methods are atheoretical they provide reasonable forecast accuracy not inferior to that of structural models. Additional advantage of their approach is that the forecasting procedure can be mostly automated and the influence of subjective decisions made in the forecasting process can be significantly reduced.