IDEAS home Printed from https://ideas.repec.org/p/bdi/wptemi/td_1027_15.html
   My bibliography  Save this paper

On the conditional distribution of euro area inflation forecast

Author

Listed:
  • Fabio Busetti

    () (Bank of Italy)

  • Michele Caivano

    () (Bank of Italy)

  • Lisa Rodano

    () (Bank of Italy)

Abstract

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.

Suggested Citation

  • Fabio Busetti & Michele Caivano & Lisa Rodano, 2015. "On the conditional distribution of euro area inflation forecast," Temi di discussione (Economic working papers) 1027, Bank of Italy, Economic Research and International Relations Area.
  • Handle: RePEc:bdi:wptemi:td_1027_15
    as

    Download full text from publisher

    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2015/2015-1027/en_tema_1027.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Clive W. J. Granger & Yongil Jeon, 2011. "The Evolution of the Phillips Curve: A Modern Time Series Viewpoint," Economica, London School of Economics and Political Science, vol. 78(309), pages 51-66, January.
    2. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    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.
    4. De Rossi, Giuliano & Harvey, Andrew, 2009. "Quantiles, expectiles and splines," Journal of Econometrics, Elsevier, vol. 152(2), pages 179-185, October.
    5. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, Fall.
    6. 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, August.
    7. 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.
    8. 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.
    9. 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.
    10. 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.).
    11. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "Inflation and monetary policy in the twentieth century," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q I, pages 22-45.
    12. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
    13. Claudia Miani & Stefano Siviero, 2010. "A non-parametric model-based approach to uncertainty and risk analysis of macroeconomic forecast," Temi di discussione (Economic working papers) 758, Bank of Italy, Economic Research and International Relations Area.
    14. Wolters Maik H. & Tillmann Peter, 2015. "The changing dynamics of US inflation persistence: a quantile regression approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 161-182, April.
    15. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    16. Marianna Riggi & Fabrizio Venditti, 2014. "Surprise! Euro area inflation has fallen," Questioni di Economia e Finanza (Occasional Papers) 237, Bank of Italy, Economic Research and International Relations Area.
    17. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, January.
    18. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    19. Fabio Busetti & Andrew Harvey, 2010. "Tests of strict stationarity based on quantile indicators," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(6), pages 435-450, November.
    20. repec:spo:wpecon:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    21. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stefano Neri & Giuseppe Ferrero, 2017. "Monetary policy in a low interest rate environment," Questioni di Economia e Finanza (Occasional Papers) 392, Bank of Italy, Economic Research and International Relations Area.
    2. repec:bla:obuest:v:79:y:2017:i:4:p:495-512 is not listed on IDEAS
    3. 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.
    4. 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.

    More about this item

    Keywords

    quantile regression; Phillips curve; time-varying distribution;

    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
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdi:wptemi:td_1027_15. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://edirc.repec.org/data/bdigvit.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.