Short term inflation forecasting: the M.E.T.A. approach

Giacomo Sbrana, Andrea Silvestrini and Fabrizio Venditti

No 1016, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: 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.

Keywords: inflation; forecasting; aggregation; state space models (search for similar items in EconPapers)
JEL-codes: C32 C53 E31 E37 (search for similar items in EconPapers)
Date: 2015-06
New Economics Papers: this item is included in nep-ecm, nep-eec, nep-for, nep-mac, nep-mon and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Short-term inflation forecasting: The M.E.T.A. approach (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_1016_15

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