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An Embarrassment of Riches: Forecasting Using Large Panels

Author

Listed:
  • Eklund, Jana

    (Bank of England)

  • Karlsson, Sune

    (Department of Business, Economics, Statistics and Informatics)

Abstract

The increasing availability of data and potential predictor variables poses new challenges to forecasters. The task of formulating a single forecasting model that can extract all the relevant information is becoming increasingly difficult in the face of this abundance of data. The two leading approaches to addressing this "embarrassment of riches" are philosophically distinct. One approach builds forecast models based on summaries of the predictor variables, such as principal components, and the second approach is analogous to forecast combination, where the forecasts from a multitude of possible models are averaged. Using several data sets we compare the performance of the two approaches in the guise of the diffusion index or factor models popularized by Stock and Watson and forecast combination as an application of Bayesian model averaging. We find that none of the methods is uniformly superior and that no method performs better than, or is outperformed by, a simple AR(p) process.

Suggested Citation

  • Eklund, Jana & Karlsson, Sune, 2007. "An Embarrassment of Riches: Forecasting Using Large Panels," Working Papers 2007:1, Örebro University, School of Business.
    • Handle: RePEc:hhs:oruesi:2007_001
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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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    Keywords

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    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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