Published in:Bank of Italy Temi di Discussione (Working Paper) ; No. 1016
Extent:
1 Online-Ressource (48 p)
Language:
English
DOI:
10.2139/ssrn.2645762
Identifier:
Origination:
Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 25, 2015 erstellt
Description:
Forecasting inflation is an important and challenging task. In this paper we assume that the core inflation components evolve as a multivariate local level process. This model, which is theoretically attractive for modelling inflation dynamics, has been used only to a limited extent to date owing to computational complications with the conventional multivariate maximum likelihood estimator, especially when the system is large. We propose the use of a method called “Moments Estimation Through Aggregation” (M.E.T.A.), which reduces computational costs significantly and delivers prompt and accurate parameter estimates, as we show in a Monte Carlo exercise. In an application to euro-area inflation we find that our forecasts compare well with those generated by alternative univariate constant and time-varying parameter models as well as with those of professional forecasters and vector autoregressions