View metadata

dc.titleForecasting Inflation in Argentina
dc.contributor.authorGaregnani, Lorena
dc.contributor.authorGómez Aguirre, Maximiliano
dc.contributor.orgunitDepartment of Research and Chief Economist
dc.coverageArgentina
dc.date.available2018-06-14T00:00:00
dc.date.issue2018-06-13T00:00:00
dc.description.abstractIn 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.
dc.format.extent23
dc.identifier.doihttp://dx.doi.org/10.18235/0001160
dc.identifier.urlhttps://publications.iadb.org/publications/english/document/Forecasting-Inflation-in-Argentina.pdf
dc.language.isoen
dc.mediumAdobe PDF
dc.publisherInter-American Development Bank
dc.subjectInflation
dc.subjectInflation Targeting
dc.subject.jelcodeC32 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes • State Space Models
dc.subject.jelcodeC53 - Forecasting and Prediction Methods • Simulation Methods
dc.subject.keywordsBayesian Vector Autoregressive;Forecasting;Prior specification;Marginal likelihood;Small-scale and large-scale models
dc.typeWorking Papers
idb.identifier.pubnumberWorking Papers
idb.operationRG-T2426
Return to Publication