Bayesian Averaging vs. Dynamic Factor Models for Forecasting Economic Aggregates with Tendency Survey Data
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.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Bayesian Averaging vs. Dynamic Factor Models for Forecasting Economic Aggregates with Tendency Survey Data ; volume:9 ; number:1 ; year:2015 ; extent:38
Economics / Journal articles. Journal articles ; 9, Heft 1 (2015) (gesamt 38)
- Creator
-
Bialowolski, Piotr
Kuszewski, Tomasz
Witkowski, Bartosz
- DOI
-
10.5018/economics-ejournal.ja.2015-31
- URN
-
urn:nbn:de:101:1-2412130951010.389632884716
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:21 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Bialowolski, Piotr
- Kuszewski, Tomasz
- Witkowski, Bartosz
Other Objects (12)
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Forecasting in large macroeconomic panels using Bayesian model averaging
Forecasting natural gas consumption in China by Bayesian Model Averaging
Bayesian dynamic quantile model averaging
Forecasting using Bayesian and information theoretic model averaging: An application to UK inflation
Forecasting using predictive likelihood model averaging
VAR Model Averaging for Multi-Step Forecasting
Sovereign risk indices and Bayesian theory averaging
Bayesian model averaging using power-expected-posterior priors
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Forecasting in large macroeconomic panels using Bayesian model averaging
Forecasting natural gas consumption in China by Bayesian Model Averaging
Bayesian dynamic quantile model averaging
Forecasting using Bayesian and information theoretic model averaging: An application to UK inflation
Forecasting using predictive likelihood model averaging
VAR Model Averaging for Multi-Step Forecasting
Sovereign risk indices and Bayesian theory averaging
Bayesian model averaging using power-expected-posterior priors
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data
Forecasting in large macroeconomic panels using Bayesian model averaging
Forecasting natural gas consumption in China by Bayesian Model Averaging
Bayesian dynamic quantile model averaging
Forecasting using Bayesian and information theoretic model averaging: An application to UK inflation
Forecasting using predictive likelihood model averaging
VAR Model Averaging for Multi-Step Forecasting
Sovereign risk indices and Bayesian theory averaging