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Forecasting key macroeconomic variables from a large number of predictors: A state space approach

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  • Arvid Raknerud
  • Terje Skjerpen
  • Anders Rygh Swensen

    ()
    (Statistics Norway)

Abstract

We use state space methods to estimate a large dynamic factor model for the Norwegian economy involving 93 variables for 1978Q2–2005Q4. The model is used to obtain forecasts for 22 key variables that can be derived from the original variables by aggregation. To investigate the potential gain in using such a large information set, we compare the forecasting properties of the dynamic factor model with those of univariate benchmark models. We find that there is an overall gain in using the dynamic factor model, but that the gain is notable only for a few of the key variables.

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Bibliographic Info

Paper provided by Research Department of Statistics Norway in its series Discussion Papers with number 504.

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Date of creation: May 2007
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Handle: RePEc:ssb:dispap:504

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Keywords: Dynamic factor model; Forecasting; State space; AR models;

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Cited by:
  1. Christian Schumacher, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 28-49, February.
  2. Arvid Raknerud & Bjørn Helge Vatne & Ketil Rakkestad, 2011. "How do banks' funding costs affect interest margins?," Discussion Papers 665, Research Department of Statistics Norway.
  3. Arvid Raknerud & Bjørn Helge Vatne, 2013. "The relations between bank-funding costs, retail rates, and loan volumes. Evidence form Norwegian microdata," Discussion Papers 742, Research Department of Statistics Norway.

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