Short-term inflation forecasting: The M.E.T.A. approach

Forecasting inflation is an important and challenging task. This paper assumes that the core inflation components evolve as a multivariate local level process. While this model is theoretically attractive for modelling inflation dynamics, its usage thus far has been limited, 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 the computational costs significantly and delivers fast 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 and multivariate models, as well as with those elicited from professional forecasters.