Forecasting inflation in Argentina - María Lorena Garegnani; Mauricio Gómez Aguirre (Banco Central de la República Argentina (BCRA), Investigaciones Económicas (ie), 2018)
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.
Keywords: C11; C13; C33; C53; 330; Bayesian vector autoregression; forecasting; prior specification; marginal likelihood; small-scale and large-scale models