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On the conditional distribution of euro area inflation forecast

Listed author(s):
  • Fabio Busetti

    ()

    (Bank of Italy)

  • Michele Caivano

    ()

    (Bank of Italy)

  • Lisa Rodano

    ()

    (Bank of Italy)

The paper uses dynamic quantile regressions to estimate and forecast the conditional distribution of euro-area inflation. As in a Phillips curve relationship we assume that inflation quantiles depend on past inflation, the output gap, and other determinants, namely oil prices and the exchange rate. We find significant time variation in the shape of the distribution. Overall, the quantile regression approach describes the distribution of inflation better than a benchmark univariate trend-cycle model with stochastic volatility, which is known to perform very well in forecasting inflation. In an out-of-sample prediction exercise, the quantile regression approach provides forecasts of the conditional distribution of inflation that are superior, overall, to those produced by the benchmark model. Averaging the distribution forecasts of the different models improves robustness and in some cases results in the greatest accuracy of distributional forecasts.

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File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2015/2015-1027/en_tema_1027.pdf
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Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 1027.

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Date of creation: Jul 2015
Handle: RePEc:bdi:wptemi:td_1027_15
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  1. Wagner Piazza Gaglianone & Luiz Renato Lima, 2014. "Constructing Optimal Density Forecasts From Point Forecast Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 736-757, 08.
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  3. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
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  12. Fabio Busetti, 2014. "Quantile aggregation of density forecasts," Temi di discussione (Economic working papers) 979, Bank of Italy, Economic Research and International Relations Area.
  13. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2015. "VAR for VaR: Measuring tail dependence using multivariate regression quantiles," Journal of Econometrics, Elsevier, vol. 187(1), pages 169-188.
  14. Andrea Stella & James H. Stock, 2012. "A state-dependent model for inflation forecasting," International Finance Discussion Papers 1062, Board of Governors of the Federal Reserve System (U.S.).
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  19. repec:spo:wpecon:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
  20. Manzan, Sebastiano & Zerom, Dawit, 2013. "Are macroeconomic variables useful for forecasting the distribution of U.S. inflation?," International Journal of Forecasting, Elsevier, vol. 29(3), pages 469-478.
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