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Quantile regression forecasts of inflation under model uncertainty

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  • Korobilis, Dimitris

Abstract

This paper examines the performance of Bayesian model averaging (BMA) methods in a quantile regression model for inflation. Different predictors are allowed to affect different quantiles of the dependent variable. Based on real-time quarterly data for the US, we show that quantile regression BMA (QR-BMA) predictive densities are superior to and better calibrated than those from BMA in the traditional regression model. In addition, QR-BMA methods also compare favorably to popular nonlinear specifications for US inflation.

Suggested Citation

  • Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:11-20
    DOI: 10.1016/j.ijforecast.2016.07.005
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
    2. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2020. "The role of real estate uncertainty in predicting US home sales growth: evidence from a quantiles-based Bayesian model averaging approach," Applied Economics, Taylor & Francis Journals, vol. 52(5), pages 528-536, January.
    3. Niango Ange Joseph Yapi, 2020. "Exchange rate predictive densities and currency risks: A quantile regression approach," EconomiX Working Papers 2020-16, University of Paris Nanterre, EconomiX.
    4. Chalmovianský, Jakub & Porqueddu, Mario & Sokol, Andrej, 2020. "Weigh(t)ing the basket: aggregate and component-based inflation forecasts for the euro area," Working Paper Series 2501, European Central Bank.
    5. S. Béreau & V. Faubert & K. Schmidt, 2018. "Explaining and Forecasting Euro Area Inflation: the Role of Domestic and Global Factors," Working papers 663, Banque de France.
    6. Milan Szabo, 2020. "Growth-at-Risk: Bayesian Approach," Working Papers 2020/3, Czech National Bank.
    7. Eguren-Martin, Fernando & Sokol, Andrej, 2019. "Attention to the tail(s): global financial conditions and exchange rate risks," Bank of England working papers 822, Bank of England.
    8. David Kohns & Tibor Szendrei, 2020. "Horseshoe Prior Bayesian Quantile Regression," Papers 2006.07655, arXiv.org.
    9. Oguzhan Cepni & Rangan Gupta & Mark E. Wohar, 2019. "Variants of Consumption-Wealth Ratios and Predictability of U.S. Government Bond Risk Premia: Old is still Gold," Working Papers 201912, University of Pretoria, Department of Economics.

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