IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this paper

Short term inflation forecasting: the M.E.T.A. approach

Listed author(s):
  • Giacomo Sbrana

    ()

    (NEOMA Business School)

  • Andrea Silvestrini

    ()

    (Bank of Italy)

  • Fabrizio Venditti

    ()

    (Bank of Italy)

Forecasting inflation is an important and challenging task. In this paper we assume that the core inflation components evolve as a multivariate local level process. This model, which is theoretically attractive for modelling inflation dynamics, has been used only to a limited extent to date owing to computational complications with the conventional multivariate maximum likelihood estimator, especially when the system is large. We propose the use of a method called “Moments Estimation Through Aggregation” (M.E.T.A.), which reduces computational costs significantly and delivers prompt and accurate parameter estimates, as we show in a Monte Carlo exercise. In an application to euro-area inflation we find that our forecasts compare well with those generated by alternative univariate constant and time-varying parameter models as well as with those of professional forecasters and vector autoregressions.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2015/2015-1016/en_tema_1016.pdf
Download Restriction: no

Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 1016.

as
in new window

Length:
Date of creation: Jun 2015
Handle: RePEc:bdi:wptemi:td_1016_15
Contact details of provider: Postal:
Via Nazionale, 91 - 00184 Roma

Web page: http://www.bancaditalia.it

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as
in new window


  1. Venditti, Fabrizio, 2013. "From oil to consumer energy prices: How much asymmetry along the way?," Energy Economics, Elsevier, vol. 40(C), pages 468-473.
  2. Abadir,Karim M. & Magnus,Jan R., 2005. "Matrix Algebra," Cambridge Books, Cambridge University Press, number 9780521537469, December.
  3. Christine Garnier & Elmar Mertens & Edward Nelson, 2015. "Trend Inflation in Advanced Economies," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 65-136, September.
  4. Gianluigi Ferrucci & Rebeca Jiménez-Rodríguez & Luca Onorantea, 2012. "Food Price Pass-Through in the Euro Area: Non-Linearities and the Role of the Common Agricultural Policy," International Journal of Central Banking, International Journal of Central Banking, vol. 8(1), pages 179-218, March.
  5. Porqueddu Mario & Venditti Fabrizio, 2014. "Do food commodity prices have asymmetric effects on euro-area inflation?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(4), pages 1-25, September.
  6. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
  7. Libero Monteforte & Gianluca Moretti, 2013. "Real‐Time Forecasts of Inflation: The Role of Financial Variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 51-61, January.
  8. Nelson, Charles R & Schwert, G William, 1977. "Short-Term Interest Rates as Predictors of Inflation: On Testing the Hypothesis That the Real Rate of Interest is Constant," American Economic Review, American Economic Association, vol. 67(3), pages 478-486, June.
  9. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
  10. Barsky, Robert B., 1987. "The Fisher hypothesis and the forecastability and persistence of inflation," Journal of Monetary Economics, Elsevier, vol. 19(1), pages 3-24, January.
  11. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
  12. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
  13. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
  14. repec:cup:cbooks:9780521822893 is not listed on IDEAS
  15. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
  16. Meyler, Aidan, 2009. "The pass through of oil prices into euro area consumer liquid fuel prices in an environment of high and volatile oil prices," Energy Economics, Elsevier, vol. 31(6), pages 867-881, November.
  17. 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.).
  18. Cogley, Timothy, 2002. "A Simple Adaptive Measure of Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(1), pages 94-113, February.
  19. Christian Kascha, 2007. "A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models," Economics Working Papers ECO2007/12, European University Institute.
  20. Poloni, Federico & Sbrana, Giacomo, 2015. "A note on forecasting demand using the multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 162(C), pages 143-150.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:bdi:wptemi:td_1016_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: ()

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.