Short Term Inflation Forecasting: The M.E.T.A. Approach

48 Pages Posted: 19 Aug 2015

Date Written: June 25, 2015

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

JEL Classification: C32, C53, E31, E37

Suggested Citation

Sbrana, Giacomo and Silvestrini, Andrea and Venditti, Fabrizio, Short Term Inflation Forecasting: The M.E.T.A. Approach (June 25, 2015). Bank of Italy Temi di Discussione (Working Paper) No. 1016, Available at SSRN: https://ssrn.com/abstract=2645762 or http://dx.doi.org/10.2139/ssrn.2645762

Giacomo Sbrana

Neoma Business School ( email )

1 Rue du Maréchal Juin
Mont Saint Aignan Cedex, 76825
France

Andrea Silvestrini (Contact Author)

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Fabrizio Venditti

Bank of Italy ( email )

Via Nazionale 91
00184 Roma
Italy

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