Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/208111 
Year of Publication: 
2018
Series/Report no.: 
IDB Working Paper Series No. IDB-WP-891
Publisher: 
Inter-American Development Bank (IDB), Washington, DC
Abstract: 
In 2016 the Central Bank of Argentina began to announce inflation targets. In this context, providing authorities with good estimates of relevant macroeconomic variables is crucial for making pertinent corrections in order to reach the desired policy goals. This paper develops a group of models to forecast inflation for Argentina, which includes autoregressive models and different scale Bayesian VARs (BVAR), and compares their relative accuracy. The results show that the BVAR model can improve the forecast ability of the univariate autoregressive benchmark's model of inflation. The Giacomini-White test indicates that a BVAR performs better than the benchmark in all forecast horizons. Statistical differences between the two BVAR model specifications (small and large-scale) are not found. However, looking at the RMSEs, one can see that the larger model seems to perform better for longer forecast horizons.
Subjects: 
Bayesian Vector Autoregressive
Forecasting
Prior specification
Marginal likelihood
Small-scale and large-scale models
JEL: 
C11
C13
C32
C53
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Working Paper

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