IDEAS home Printed from
   My bibliography  Save this article

Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models


  • Gerdesmeier Dieter

    () (European Central Bank, Sonnemannstraße 20, 60314 Frankfurt am Main, Germany; Frankfurt School of Finance and Management, Adickesallee 32-34, 60322 Frankfurt am Main, Germany)

  • Roffia Barbara

    () (European Central Bank, Sonnemannstraße 20, 60314 Frankfurt am Main, Germany)

  • Reimers Hans-Eggert

    () (Hochschule Wismar, Wismar Business School, Postfach 1210, 23952Wismar, Germany)


Forecasting inflation is of key relevance for central banks, not least because the objective of low and stable inflation is embodied in most central banks’ mandates and the monetary policy transmission mechanism is well known to be subject to long and variable lags. To our best knowledge, central banks around the world use conditional as well as unconditional forecasts for such purposes. Turning to unconditional forecasts, these can be derived on the basis of structural and non-structural models. Among the latter, vector autoregressive (VAR)-models are among the most popular tools.

Suggested Citation

  • Gerdesmeier Dieter & Roffia Barbara & Reimers Hans-Eggert, 2017. "Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models," Folia Oeconomica Stetinensia, Sciendo, vol. 17(2), pages 19-34, December.
  • Handle: RePEc:vrs:foeste:v:17:y:2017:i:2:p:19-34:n:2

    Download full text from publisher

    File URL:
    Download Restriction: no

    More about this item


    inflation forecasts; euro area; Bayesian VAR;

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General


    Access and download statistics


    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:vrs:foeste:v:17:y:2017:i:2:p:19-34:n:2. 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: (Peter Golla). General contact details of provider: .

    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.

    We have no references for this item. You can help adding them by using 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.