Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/121391
Authors: 
Bialowolski, Piotr
Kuszewski, Tomasz
Witkowski, Bartosz
Year of Publication: 
2015
Citation: 
[Journal:] Economics: The Open-Access, Open-Assessment E-Journal [Volume:] 9 [Issue:] 2015-31 [Pages:] 1-37
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.
Subjects: 
Bayesian averaging of classical estimates
dynamic factor models
tendency survey data
forecasting
JEL: 
C10
C38
C83
E32
E37
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/3.0/
Document Type: 
Article

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