Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/217594 
Year of Publication: 
2016
Citation: 
[Journal:] Journal of Central Banking Theory and Practice [ISSN:] 2336-9205 [Volume:] 5 [Issue:] 2 [Publisher:] De Gruyter Open [Place:] Warsaw [Year:] 2016 [Pages:] 81-99
Publisher: 
De Gruyter Open, Warsaw
Abstract: 
We evaluate the forecasting performance of four competing models for short-term macroeconomic forecasting: the traditional VAR, small scale Bayesian VAR, Factor Augmented VAR and Bayesian Factor Augmented VAR models. Using Armenian quarterly actual macroeconomic time series from 1996Q1 – 2014Q4, we estimate parameters of four competing models. Based on the out-of-sample recursive forecast evaluations and using root mean squared error (RMSE) criterion we conclude that small scale Bayesian VAR and Bayesian Factor Augmented VAR models are more suitable for short-term forecasting than traditional unrestricted VAR model.
Subjects: 
vector autoregression
Bayesian estimation
principal components
recursive regression
forecast evaluation
macroeconomic indicators
Armenia.
JEL: 
C11
C13
C52
C53
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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