Artikel
Bayesian forecasts combination to improve the Romanian inflation predictions based on econometric models
There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts' forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel), National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts' predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.
- Language
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Englisch
- Bibliographic citation
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Journal: UTMS Journal of Economics ; ISSN: 1857-6982 ; Volume: 5 ; Year: 2014 ; Issue: 2 ; Pages: 131-140 ; Skopje: University of Tourism and Management
- Classification
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Wirtschaft
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
Model Construction and Estimation
Model Evaluation, Validation, and Selection
Forecasting Models; Simulation Methods
- Subject
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Bayesian forecasts combination
forecasts accuracy
prior
shrinkage parameter
econometric model.
- Event
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Geistige Schöpfung
- (who)
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Simionescu, Mihaela
- Event
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Veröffentlichung
- (who)
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University of Tourism and Management
- (where)
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Skopje
- (when)
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2014
- Handle
- Last update
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10.03.2025, 11:41 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Simionescu, Mihaela
- University of Tourism and Management
Time of origin
- 2014
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