English, Article, Journal or magazine article edition: Building composite leading indexes in a dynamic factor model framework: a new proposal Massimiliano Serati; Gianni Amisano

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Bookmark: http://trove.nla.gov.au/version/37053
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Building composite leading indexes in a dynamic factor model framework: a new proposal
Author
  • Massimiliano Serati
  • Gianni Amisano
Physical Description
  • preprint
Notes
  • One of the most problematic aspects in the work of policy makers and practitioners is having efficient forecasting tools combining two seemingly incompatible features: ease of use and completeness of the information set underlying the forecasts. Econometric literature provides different answers to these needs: Dynamic Factor Models (DFMs) optimally exploit the information coming from large datasets; composite leading indexes represent an immediate and flexible tool to anticipate the future evolution of a phenomenon. Curiously, the recent DFM literature has either ignored the construction of leading indexes or has made unsatisfactory choices as regards the criteria for aggregating the index components and the identification of factors that feed the index. This paper fills the gap and proposes a multi-step procedure for building composite leading indexes within a DFM framework. Once selected the target economic variable and estimated a DFM based on a large target-oriented dataset, we identify the common factor shocks through sign restrictions on the impact multipliers and simulate the structural form of the model. The Forecast Error Variance Decompositions obtained over a k steps-ahead simulation horizon define k sets of weights for aggregating factors (in a different way depending on the leading horizon) in order to get composite leading indexes. This procedure is used for a very preliminar empirical exercise aimed at forecasting crude nominal oil prices. The results seem to be encouraging and support the validity of the proposal: we generate a wide range of horizon-specific leading indexes with appreciable forecasting performances.
  • RePEc:liu:liucec:212
Language
  • English
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