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Generalised Density Forecast Combinations

Author

Listed:
  • Fawcett, Nicholas

    (Bank of England)

  • Kapetanios, George

    (Queen Mary, University of London)

  • Mitchell, James

    (University of Warwick)

  • Price, Simon

    (Bank of England)

Abstract

Density forecast combinations are becoming increasingly popular as a means of improving forecast ‘accuracy’, as measured by a scoring rule. In this paper we generalise this literature by letting the combination weights follow more general schemes. Sieve estimation is used to optimise the score of the generalised density combination where the combination weights depend on the variable one is trying to forecast. Specific attention is paid to the use of piecewise linear weight functions that let the weights vary by region of the density. We analyse these schemes theoretically, in Monte Carlo experiments and in an empirical study. Our results show that the generalised combinations outperform their linear counterparts.

Suggested Citation

  • Fawcett, Nicholas & Kapetanios, George & Mitchell, James & Price, Simon, 2013. "Generalised Density Forecast Combinations," EMF Research Papers 05, Economic Modelling and Forecasting Group.
  • Handle: RePEc:wrk:wrkemf:05
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    2. Emilio Zanetti Chini, 2018. "Forecaster’s utility and forecasts coherence," DEM Working Papers Series 145, University of Pavia, Department of Economics and Management.
    3. Szabolcs Deák & Paul Levine & Afrasiab Mirza & Joseph Pearlman, 2019. "Designing Robust Monetary Policy Using Prediction Pools," School of Economics Discussion Papers 1219, School of Economics, University of Surrey.
    4. Roberto Casarin & Fabrizio Leisen & German Molina & Enrique ter Horst, 2014. "A Bayesian Beta Markov Random Field Calibration of the Term Structure of Implied Risk Neutral Densities," Papers 1409.1956, arXiv.org.
    5. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    6. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2019. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Working Papers No 01/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    7. Del Negro, Marco & Hasegawa, Raiden B. & Schorfheide, Frank, 2016. "Dynamic prediction pools: An investigation of financial frictions and forecasting performance," Journal of Econometrics, Elsevier, vol. 192(2), pages 391-405.
    8. Knüppel, Malte & Krüger, Fabian, 2017. "Forecast Uncertainty, Disagreement, and Linear Pools of Density Forecasts," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168294, Verein für Socialpolitik / German Economic Association.
    9. repec:gam:jecnmx:v:4:y:2016:i:1:p:17:d:65855 is not listed on IDEAS
    10. Knüppel, Malte & Krüger, Fabian, 2017. "Forecast Uncertainty, Disagreement, and Linear Pools of Density Forecasts," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168294, Verein für Socialpolitik / German Economic Association.
    11. Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 81568, University Library of Munich, Germany.
    12. Roberto Casarin & Federico Bassetti & Francesco Ravazzolo, 2015. "Bayesian Nonparametric Calibration and Combination of Predictive Distributions," Working Papers 2015:04, Department of Economics, University of Venice "Ca' Foscari".
    13. repec:eee:ejores:v:267:y:2018:i:3:p:877-890 is not listed on IDEAS
    14. Roberto Casarin & Giulia Mantoan & Francesco Ravazzolo, 2016. "Bayesian Calibration of Generalized Pools of Predictive Distributions," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-24, March.
    15. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Domenico Sartore, 2018. "A scoring rule for factor and autoregressive models under misspecification," Working Papers 2018:18, Department of Economics, University of Venice "Ca' Foscari".
    16. repec:eee:econom:v:210:y:2019:i:1:p:155-169 is not listed on IDEAS
    17. Roberto Casarin, 2014. "A Note on Tractable State-Space Model for Symmetric Positive-Definite Matrices," Working Papers 2014:23, Department of Economics, University of Venice "Ca' Foscari".
    18. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
    19. repec:eee:asieco:v:60:y:2019:i:c:p:45-68 is not listed on IDEAS
    20. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    21. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    22. repec:bla:obuest:v:79:y:2017:i:4:p:495-512 is not listed on IDEAS
    23. Fabio Busetti, 2017. "Quantile Aggregation of Density Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 495-512, August.
    24. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.

    More about this item

    Keywords

    Density Forecasting ; Model Combination ; Scoring Rules JEL Classification Numbers: C53;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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