Journal Article

No. 2015-31 | October 14, 2015
Bayesian Averaging vs. Dynamic Factor Models for Forecasting Economic Aggregates with Tendency Survey Data PDF Icon

Abstract

The article compares forecast quality from two atheoretical models. Neither method assumed a priori causality and forecasts were generated without additional assumptions about regressors. Tendency survey data was used within the Bayesian averaging of classical estimates (BACE) framework and dynamic factor models (DFM). Two methods for regressor selection were applied within the BACE framework: frequentist averaging (BA) and frequentist (BF) with a collinearity-corrected version of the latter (BFC). Since models yielded multiple forecasts for each period, an approach to combine them was implemented. Results were assessed using in- and out-of-sample prediction errors. Although results did not vary significantly, best performance was observed from Bayesian models adopting the frequentist approach. Forecast of the unemployment rate were generated with the highest precision, followed by rate of GDP growth and CPI. It can be concluded that although these methods are atheoretical, they provide reasonable forecast accuracy, no worse to that expected from structural models. A further advantage to this approach is that much of the forecast procedure can be automated and much influence from subjective decisions avoided.

Codes are available upon request.

JEL Classification

C10 C38 C83 E32 E37

Citation

Piotr Bialowolski, Tomasz Kuszewski, and Bartosz Witkowski (2015). Bayesian Averaging vs. Dynamic Factor Models for Forecasting Economic Aggregates with Tendency Survey Data. Economics: The Open-Access, Open-Assessment E-Journal, 9 (2015-31): 1—37. http://dx.doi.org/10.5018/economics-ejournal.ja.2015-31

Assessment

Downloads: 1509 (Journalarticle: 686, Discussionpaper: 823)
external link Search this article at Google Scholar



Comments and Questions


Filed under: