Forecasting Italian Inflation with Large Datasets and Many Models

Abstract

The aim of this paper is to propose a new method for forecasting Italian
inflation. We expand on a standard factor model framework (see Stock and
Watson (1998)) along several dimensions. To start with we pay special
attention to the modeling of the autoregressive component of the inflation.
Second, we apply forecast combination (Granger (2000) and Pesaran and
Timmermann (2001)) and generate our forecast by averaging the predictions
of a large number of models. Third, we allow for time variation in parameters
by applying rolling regression techniques, with a window of three-years of
monthly data. Backtesting shows that our strategy outperforms both the
benchmark model (i.e. a factor model which does not allow for model
uncertainty) and additional univariate (ARMA) and multivariate (VAR)
models. Our strategy proves to improve on alternative models also when
applied to turning point prediction.