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An Embarrassment of Riches: Forecasting Using Large Panels

Author

Listed:
  • Jana Eklund
  • Sune Karlsson
Abstract
The problem of having to select a small subset of predictors from a large number of useful variables can be circumvented nowadays in forecasting. One possibility is to efficiently and systematically evaluate all predictors and almost all possible models that these predictors in combination can give rise to. The idea of combining forecasts from various indicator models by using Bayesian model averaging is explored, and compared to diffusion indexes, another method using large number of predictors to forecast. In addition forecasts based on the median model are considered.

Suggested Citation

  • Jana Eklund & Sune Karlsson, 2007. "An Embarrassment of Riches: Forecasting Using Large Panels," Economics wp34, Department of Economics, Central bank of Iceland.
    • Handle: RePEc:ice:wpaper:wp34
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    File URL: http://www.sedlabanki.is/lisalib/getfile.aspx?itemid=5128
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    References listed on IDEAS

    as
    1. Sune Karlsson & Tor Jacobson, 2004. "Finding good predictors for inflation: a Bayesian model averaging approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 479-496.
    2. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    3. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    4. Gary Koop & Simon Potter, 2004. "Forecasting in dynamic factor models using Bayesian model averaging," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 550-565, December.
    5. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
    6. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Riso, Luigi & Vacca, Gianmarco, 2024. "Sentiment dynamics and volatility: A study based on GARCH-MIDAS and machine learning," Finance Research Letters, Elsevier, vol. 62(PB).
    2. Jana Eklund & Sune Karlsson, 2007. "Forecast Combination and Model Averaging Using Predictive Measures," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 329-363.
    3. Yin-Wong Cheung & Shi He, 2019. "Truths and Myths About RMB Misalignment: A Meta-analysis," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 61(3), pages 464-492, September.
    4. repec:zbw:bofitp:2019_003 is not listed on IDEAS
    5. Yin-Wong Cheung & Shi He, 2019. "Truths and Myths About RMB Misalignment: A Meta-analysis," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 61(3), pages 464-492, September.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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