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

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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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  • Arvid Raknerud & Terje Skjerpen & Anders Rygh Swensen, 2007. "Forecasting key macroeconomic variables from a large number of predictors: A state space approach," Discussion Papers 504, Statistics Norway, Research Department.
    • Handle: RePEc:ssb:dispap:504
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    File URL: https://www.ssb.no/a/publikasjoner/pdf/DP/dp504.pdf
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    Cited by:

    1. Arvid Raknerud & Bjørn Helge Vatne, 2013. "The relations between bank-funding costs, retail rates, and loan volumes. Evidence form Norwegian microdata," Discussion Papers 742, Statistics Norway, Research Department.
    2. Arvid Raknerud & Bjørn Helge Vatne, 2012. "The relation between banks' funding costs, retail rates and loan volumes: An analysis of Norwegian bank micro data," Working Paper 2012/17, Norges Bank.
    3. Arvid Raknerud & Bjørn Helge Vatne & Ketil Rakkestad, 2011. "How do banks' funding costs affect interest margins?," Discussion Papers 665, Statistics Norway, Research Department.
    4. Schumacher Christian, 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), De Gruyter, vol. 231(1), pages 28-49, February.
    5. Kyle E. Binder & Mohsen Pourahmadi & James W. Mjelde, 2020. "The role of temporal dependence in factor selection and forecasting oil prices," Empirical Economics, Springer, vol. 58(3), pages 1185-1223, March.

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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