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A fast algorithm for finding the confidence set of large collections of models

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  • Sylvain Barde

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Abstract

The paper proposes a new algorithm for finding the confidence set of a collection of forecasts or prediction models. Existing numerical implementations for finding the confidence set use an elimination approach where one starts with the full collection of models and successively eliminates the worst performing until the null of equal predictive ability is no longer rejected at a given confidence level. The intuition behind the proposed implementation lies in reversing the process: one starts with a collection of two models and as models are successively added to the collection both the model rankings and p-values are updated. The first benefit of this updating approach is a reduction of one polynomial order in both the time complexity and memory cost of finding the confidence set of a collection of M models, falling respectively from O (M3) to O (M2) and from O (M2) to O (M). This theoretical prediction is confirmed by a Monte Carlo benchmarking analysis of the algorithms. The second key benefit of the updating approach is that it intuitively allows for further models to be added at a later point in time, thus enabling collaborative efforts using the model confidence set procedure.

Suggested Citation

  • Sylvain Barde, 2015. "A fast algorithm for finding the confidence set of large collections of models," Studies in Economics 1519, School of Economics, University of Kent.
  • Handle: RePEc:ukc:ukcedp:1519
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    File URL: ftp://ftp.ukc.ac.uk/pub/ejr/RePEc/ukc/ukcedp/1519.pdf
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    References listed on IDEAS

    as
    1. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    2. Zeynep Iltuzer & Oktay Tas, 2013. "The Forecasting Performances of Volatility Models in Emerging Stock Markets: Is a Generalization Really Possible?," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 3(2), pages 1-4.
    3. Mauro Bernardi & Leopoldo Catania, 2014. "The Model Confidence Set package for R," Papers 1410.8504, arXiv.org.
    4. Caporin, Massimiliano & McAleer, Michael, 2014. "Robust ranking of multivariate GARCH models by problem dimension," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 172-185.
    5. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    6. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    7. Neumann, Michael & Skiadopoulos, George, 2013. "Predictable Dynamics in Higher-Order Risk-Neutral Moments: Evidence from the S&P 500 Options," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(03), pages 947-977, June.
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    More about this item

    Keywords

    Model selection; model confidence set; bootstrapped statistics;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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