IDEAS home Printed from https://ideas.repec.org/p/fip/fedrwp/94-08.html
   My bibliography  Save this paper

Forecasts of inflation for VAR models

Author

Listed:
  • Roy H. Webb

Abstract

Why are forecasts of inflation from VAR models so much worse then their forecasts of real variables? This paper documents that relatively poor performance, and finds that the price equation of a VAR model fitted to U.S. postwar data is poorly specified. Statistical work by other authors has found that coefficients in such price equations may not be constant. Based on specific monetary actions, two changes in monetary policy regimes are proposed. Accounting for those two shifts yields significantly more accurate forecasts and lessens the evidence of misspecification.

Suggested Citation

  • Roy H. Webb, 1994. "Forecasts of inflation for VAR models," Working Paper 94-08, Federal Reserve Bank of Richmond.
  • Handle: RePEc:fip:fedrwp:94-08
    as

    Download full text from publisher

    File URL: http://www.richmondfed.org/publications/research/working_papers/1994/wp_94-8.cfm
    Download Restriction: no

    File URL: http://www.richmondfed.org/publications/research/working_papers/1994/pdf/wp94-8.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    2. 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.
    3. Thomas,Alex M., 2021. "Macroeconomics," Cambridge Books, Cambridge University Press, number 9781108731997.
    4. Balke, Nathan S. & Fomby, Thomas B., 1991. "Shifting trends, segmented trends, and infrequent permanent shocks," Journal of Monetary Economics, Elsevier, vol. 28(1), pages 61-85, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    2. Xiaojie Xu, 2017. "The rolling causal structure between the Chinese stock index and futures," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(4), pages 491-509, November.
    3. Sager, Michael & Taylor, Mark P., 2014. "Generating currency trading rules from the term structure of forward foreign exchange premia," Journal of International Money and Finance, Elsevier, vol. 44(C), pages 230-250.
    4. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    5. Pär Österholm, 2005. "The Taylor Rule: A Spurious Regression?," Bulletin of Economic Research, Wiley Blackwell, vol. 57(3), pages 217-247, July.
    6. Richard H. Clarida & Lucio Sarno & Mark P. Taylor & Giorgio Valente, 2006. "The Role of Asymmetries and Regime Shifts in the Term Structure of Interest Rates," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1193-1224, May.
    7. Kieran Burgess & Nicholas Rohde, 2013. "Can Exchange Rates Forecast Commodity Prices? Recent Evidence using Australian Data," Economics Bulletin, AccessEcon, vol. 33(1), pages 511-518.
    8. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    9. Adusei Jumah & Robert M. Kunst, 2016. "Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging," Applied Economics, Taylor & Francis Journals, vol. 48(45), pages 4366-4378, September.
    10. Huang, Bwo-Nung & Yang, C.W. & Hwang, M.J., 2009. "The dynamics of a nonlinear relationship between crude oil spot and futures prices: A multivariate threshold regression approach," Energy Economics, Elsevier, vol. 31(1), pages 91-98, January.
    11. MacDonald, Ronald & Marsh, Ian W., 2004. "Currency spillovers and tri-polarity: a simultaneous model of the US dollar, German mark and Japanese yen," Journal of International Money and Finance, Elsevier, vol. 23(1), pages 99-111, February.
    12. Kunze, Frederik, 2017. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," University of Göttingen Working Papers in Economics 326, University of Goettingen, Department of Economics.
    13. Francisco Estrada & Pierre Perron, 2019. "Breaks, Trends and the Attribution of Climate Change: A Time-Series Analysis," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 42(83), pages 1-31.
    14. Kladívko, Kamil & Österholm, Pär, 2021. "Do market participants’ forecasts of financial variables outperform the random-walk benchmark?," Finance Research Letters, Elsevier, vol. 40(C).
    15. Maximo Camacho & Gabriel Perez-Quiros, 2002. "This is what the leading indicators lead," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(1), pages 61-80.
    16. Kearney, Fearghal & Cummins, Mark & Murphy, Finbarr, 2019. "Using extracted forward rate term structure information to forecast foreign exchange rates," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 1-14.
    17. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    18. Haugom, Erik & Ullrich, Carl J., 2012. "Market efficiency and risk premia in short-term forward prices," Energy Economics, Elsevier, vol. 34(6), pages 1931-1941.
    19. El-Shazly, Alaa, 2013. "Electricity demand analysis and forecasting: A panel cointegration approach," Energy Economics, Elsevier, vol. 40(C), pages 251-258.
    20. Clostermann, Jörg & Seitz, Franz, 2005. "Are bond markets really overpriced: The case of the US," Arbeitsberichte – Working Papers 11, Technische Hochschule Ingolstadt (THI).

    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:fip:fedrwp:94-08. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christian Pascasio (email available below). General contact details of provider: https://edirc.repec.org/data/frbrius.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.