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Real-time Inflation Forecast Densities from Ensemble Phillips Curves

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Listed:
  • Anthony Garratt
  • James Mitchell
  • Shaun P. Vahey
  • Elizabeth C. Wakerly

    (Department of Economics, Mathematics & Statistics, Birkbeck)

Abstract

A popular macroeconomic forecasting strategy takes combinations across many models to hedge against model instabilities of unknown timing; see (among others) Stock andWatson (2004) and Clark and McCracken (2009). In this paper, we examine the effectiveness of recursive-weight and equal-weight combination strategies for density forecasting using a time-varying Phillips curve relationship between inflation and the output gap. The densities reflect the uncertainty across a large number of models using many statistical measures of the output gap, allowing for a single structural break of unknown timing. We use real-time data for the US, Australia, New Zealand and Norway. Our main finding is that the recursive-weight strategy performs well across the real-time data sets, consistently giving well-calibrated forecast densities. The equal-weight strategy generates poorly-calibrated forecast densities for the US and Australian samples. There is little difference between the two strategies for our New Zealand and Norwegian data. We also find that the ensemble modeling approach performs more consistently with real-time data than with revised data in all four countries.

Suggested Citation

  • Anthony Garratt & James Mitchell & Shaun P. Vahey & Elizabeth C. Wakerly, 2009. "Real-time Inflation Forecast Densities from Ensemble Phillips Curves," Birkbeck Working Papers in Economics and Finance 0910, Birkbeck, Department of Economics, Mathematics & Statistics.
  • Handle: RePEc:bbk:bbkefp:0910
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Ravazzolo Francesco & Vahey Shaun P., 2014. "Forecast densities for economic aggregates from disaggregate ensembles," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(4), pages 1-15, September.
    2. McDonald, Christopher & Thamotheram, Craig & Vahey, Shaun P. & Wakerly, Elizabeth C., 2015. "Assessing the Economic Value of Probabilistic Forecasts in the Presence of an Inflation Target," EMF Research Papers 09, Economic Modelling and Forecasting Group.
    3. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters,in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8 Bank for International Settlements.
    4. Gian Luigi Mazzi & James Mitchell & Gaetana Montana, 2014. "Density Nowcasts and Model Combination: Nowcasting Euro-Area GDP Growth over the 2008–09 Recession," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 233-256, April.
    5. 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.
    6. Pauwels, Laurent & Vasnev, Andrey, 2014. "Forecast combination for U.S. recessions with real-time data," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 138-148.
    7. repec:syb:wpbsba:01/2013 is not listed on IDEAS
    8. Siemsen, Thomas & Vilsmeier, Johannes, 2018. "On a quest for robustness: About model risk, randomness and discretion in credit risk stress tests," Discussion Papers 31/2018, Deutsche Bundesbank.
    9. Bjørnland, Hilde C. & Gerdrup, Karsten & Jore, Anne Sofie & Smith, Christie & Thorsrud, Leif Anders, 2011. "Weights and pools for a Norwegian density combination," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 61-76, January.
    10. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    11. 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.
    12. Mitchell, James, 2013. "The Recalibrated and Copula Opinion Pools," EMF Research Papers 02, Economic Modelling and Forecasting Group.
    13. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Probability Forecasting for Inflation Warnings from the Federal Reserve," EMF Research Papers 07, Economic Modelling and Forecasting Group.

    More about this item

    JEL classification:

    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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