IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper

New methods for forecasting inflation and its sub-components: application to the USA

  • Janine Aron
  • John Muellbauer

Forecasts are presented for the 12-month ahead US rate of inflation measured by the chain weighted personal consumer expenditure deflator, PC, and its three main components: non-durable goods, durable goods and services. Monthly models are estimated for 1974 to 1999, and pseudo out-of-sample forecasting performance is examined for 2000-2007. Alternative forecasting approaches for a number of different information sets are compared with benchmark univariate autoregressive models. In general, substantial out-performance is demonstrated for the aggregate and components models relative to benchmark models. The combination of equilibrium correction terms, which bring gradual adjustment of relative prices into the inflation process, and non-linearities, to proxy state dependence in the inflation process, is shown to contribute importantly to this out-performance. There is also evidence that forecast pooling or averaging improves forecast performance. The indirect forecasts constructed by weighting the three component forecasts are compared with the direct forecasts from the aggregate PC. In most cases, the indirect method outperforms the direct method. A key innovation is to compare standard AR or VAR methods of using an information criterion to select the large length, with a parameterization in which longer lags appear in parsimonious forms. Another is to compare general unrestricted models with corresponding parsimonious models selected by Autometrics, Doornik (2008).

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.economics.ox.ac.uk/materials/working_papers/paper406.pdf
Download Restriction: no

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 406.

as
in new window

Length:
Date of creation: 01 Oct 2008
Date of revision:
Handle: RePEc:oxf:wpaper:406
Contact details of provider: Postal:
Manor Rd. Building, Oxford, OX1 3UQ

Web page: http://www.economics.ox.ac.uk/
Email:


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. David Hendry & Michael P. Clements, 2001. "Economic Forecasting: Some Lessons from Recent Research," Economics Series Working Papers 78, University of Oxford, Department of Economics.
  2. Jon Frye & Robert J. Gordon, 1980. "The Variance and Acceleration of Inflation in the 1970s: Alternative Explanatory Models and Methods," NBER Working Papers 0551, National Bureau of Economic Research, Inc.
  3. Ricardo Reis, 2005. "Inattentive Producers," 2005 Meeting Papers 290, Society for Economic Dynamics.
  4. Sharon Kozicki, 1997. "Predicting real growth and inflation with the yield spread," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 39-57.
  5. Luis J. Álvarez & Emmanuel Dhyne & Marco Hoeberichts & C. Kwapil & Hervé Le Bihan & Patrick Lünnemann & Fernando Martins & R. Sabbatini & H. Stahl & Philip Vermeulen & Jouko Vilmunen, 2006. "Sticky Prices in The Euro Area: a Summary of New Micro Evidence," Working Papers w200605, Banco de Portugal, Economics and Research Department.
  6. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-47, July-Sept.
  7. Hendry, David F & Hubrich, Kirstin, 2006. "Forecasting Economic Aggregates by Disaggregates," CEPR Discussion Papers 5485, C.E.P.R. Discussion Papers.
  8. John V. Duca & Carl M. Campbell, 2007. "The impact of evolving labor practices and demographics on U.S. inflation and unemployment," Working Papers 0702, Federal Reserve Bank of Dallas.
  9. Benalal, Nicholai & Diaz del Hoyo, Juan Luis & Landau, Bettina & Roma, Moreno & Skudelny, Frauke, 2004. "To aggregate or not to aggregate? Euro area inflation forecasting," Working Paper Series 0374, European Central Bank.
  10. Gabriel Moser & Fabio Rumler & Johann Scharler, 2004. "Forecasting Austrian Inflation," Working Papers 91, Oesterreichische Nationalbank (Austrian Central Bank).
  11. Ard Reijer & Peter Vlaar, 2006. "Forecasting Inflation: An Art as Well as a Science!," De Economist, Springer, vol. 154(1), pages 19-40, 03.
  12. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-89, June.
  13. Weiss, Andrew A., 1991. "Multi-step estimation and forecasting in dynamic models," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 135-149.
  14. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  15. Pesaran, M Hashem & Pierse, Richard G & Kumar, Mohan S, 1989. "Econometric Analysis of Aggregation in the Context of Linear Prediction Models," Econometrica, Econometric Society, vol. 57(4), pages 861-88, July.
  16. Van Garderen, K. J. & Lee, K. & Pesaran M., 1998. "Cross-sectional Aggregation of Non-linear Models," Cambridge Working Papers in Economics 9803, Faculty of Economics, University of Cambridge.
  17. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, October.
  18. Marvin J. Barth III & Valerie A. Ramey, 2002. "The Cost Channel of Monetary Transmission," NBER Chapters, in: NBER Macroeconomics Annual 2001, Volume 16, pages 199-256 National Bureau of Economic Research, Inc.
  19. Richard Peach & Robert W. Rich & Alexis Antoniades, 2004. "The historical and recent behavior of goods and services inflation," Economic Policy Review, Federal Reserve Bank of New York, issue Dec, pages 19-31.
  20. Robert W. Rich & Charles Steindel, 2005. "A review of core inflation and an evaluation of its measures," Staff Reports 236, Federal Reserve Bank of New York.
  21. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  22. Fama, Eugene F., 1990. "Term-structure forecasts of interest rates, inflation and real returns," Journal of Monetary Economics, Elsevier, vol. 25(1), pages 59-76, January.
  23. Ignazio Angeloni & Luc Aucremanne & Michael Ehrmann & Jordi Galí & Andrew Levin & Frank Smets, 2006. "New Evidence on Inflation Persistence and Price Stickiness in the Euro Area: Implications for Macro Modeling," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 562-574, 04-05.
  24. Eytan Sheshinski & Yoram Weiss, 1977. "Inflation and Costs of Price Adjustment," Review of Economic Studies, Oxford University Press, vol. 44(2), pages 287-303.
  25. Espasa, Antoni & Albacete, Rebeca, 2005. "Forecasting inflation in the euro area using monthly time series models and quarterly econometric models," DES - Working Papers. Statistics and Econometrics. WS ws050401, Universidad Carlos III de Madrid. Departamento de Estadística.
  26. Clements, Michael P & Hendry, David F, 1996. "Multi-step Estimation for Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 657-84, November.
  27. Michael P. Clements & David F. Hendry, 2002. "Modelling methodology and forecast failure," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 319-344, 06.
  28. James H. Stock & Mark W. Watson, 2001. "Forecasting output and inflation: the role of asset prices," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  29. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
  30. Lutkepohl, Helmut, 1984. "Forecasting Contemporaneously Aggregated Vector ARMA Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 201-14, July.
  31. Roberts, John M., 1997. "Is inflation sticky?," Journal of Monetary Economics, Elsevier, vol. 39(2), pages 173-196, July.
  32. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
  33. Kohn, Robert, 1982. "When is an aggregate of a time series efficiently forecast by its past?," Journal of Econometrics, Elsevier, vol. 18(3), pages 337-349, April.
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:oxf:wpaper:406. 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: (Monica Birds)

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.