Forecasting Italian inflation with large datasets and many models
AbstractThe 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 outrperforms 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.
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Bibliographic InfoPaper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number 269.
Date of creation: 2004
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-10-18 (All new papers)
- NEP-ECM-2004-10-18 (Econometrics)
- NEP-ETS-2004-10-18 (Econometric Time Series)
- NEP-MAC-2004-10-18 (Macroeconomics)
- NEP-MON-2004-10-18 (Monetary Economics)
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- Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1.
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