Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/202488
Authors: 
Garegnani, María Lorena
Gómez Aguirre, Mauricio
Year of Publication: 
2018
Series/Report no.: 
Economic Research Working Papers 79
Abstract: 
During the year 2016, the Central Bank of Argentina has begun to announce inflation targets. In this context, providing the authorities of good estimates of relevant macroeconomic variables turns out to be crucial to make the pertinent corrections 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 larger forecast horizons.
Subjects: 
Bayesian vector autoregression
forecasting
prior specification
marginal likelihood
small-scale and large-scale models
JEL: 
C11
C13
C33
C53
Document Type: 
Working Paper

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