Seminario de Economía | Maximiliano Gómez Aguirre y Luis Libonatti (BCRA)
Jueves 22/11 a las 15:30 en Reconquista 250, 9° piso.
El próximo jueves 22 de noviembre Maximiliano Gómez Aguirre y Luis Libonatti presentarán el trabajo Forecasting Inflation in Argentina: A Comparison of Different Models del que son coautores junto con Laura D' Amato, Lorena Garegnani y Ariel Krysa, integrantes de la subgerencia general de Investigaciones Económicas.
Este ciclo de seminarios invita a investigadores externos o internos a presentar sus trabajos ante una audiencia especializada.
Inscripción previa | investig@bcra.gob.ar.
Maximiliano Gómez Aguirre es licenciado en Economía por la Universidad Católica de La Plata y magister en Economía y Finanzas Públicas de la Universidad Nacional de La Plata. Se desempeña en la gerencia de Modelos y Pronósticos Macroeconómicos.
Luis Libonatti es licenciado en Economía por la Universidad Católica Argentina y está cursando la maestría en Economía de la Universidad Torcuato Di Tella. Se desempeña en la gerencia de Modelos y Pronósticos Macroeconómicos.
Abstract
In general, central banks are concerned with keeping the inflation rate stable while also sustaining output close to an efficient level. Under “inflation targeting”, forecasts of the evolution of the general price level are an essential input for policy decisions and these are usually released in quarterly “Inflation Reports”. The costs and benefits of transparency in monetary policy are widely debated, but the need for a central bank to incorporate forecasts of future inflation is broadly agreed. In short, forecasting inflation is of foremost importance to households, businesses, and policymakers. In 2016, the Central Bank of Argentina began announcing and inflation targeting scheme. In this context, providing the authorities with 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 in Argentina and conducts a comparison of their predictive ability at different horizons. Our variety of models includes: (i) univariate time series models, (ii) VARs, Bayesian VARs and Time-Varying Parameter VARs, and (iii) conventional New Keynesian Phillips Curves including one that incorporates money to evaluate its information content as a predictor of inflation. We compare the predictive performance of the different methods using the Giacomini-White test over the relevant horizons for monetary policy decisions.
20 de noviembre de 2018
