IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v34y2015i8p619-648.html
   My bibliography  Save this article

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

Author

Listed:
  • Gabriele Di Filippo

Abstract

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.

Suggested Citation

  • Gabriele Di Filippo, 2015. "Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(8), pages 619-648, December.
  • Handle: RePEc:wly:jforec:v:34:y:2015:i:8:p:619-648
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Tesi Aliaj & Milos Ciganovic & Massimiliano Tancioni, 2023. "Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 464-480, April.
    2. Patricia Toledo & Roberto Duncan, 2024. "Forecasting food price inflation during global crises," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1087-1113, July.
    3. Hassani, Hossein & Silva, Emmanuel Sirimal, 2018. "Forecasting UK consumer price inflation using inflation forecasts," Research in Economics, Elsevier, vol. 72(3), pages 367-378.
    4. Xiao, Jiang & Wang, Minggang & Tian, Lixin & Zhen, Zaili, 2018. "The measurement of China’s consumer market development based on CPI data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 664-680.
    5. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    6. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
    7. Anastasiia Pankratova, 2024. "Forecasting Key Macroeconomic Indicators Using DMA and DMS Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 32-52, March.
    8. Krzysztof DRACHAL, 2020. "Forecasting the Inflation Rate in Poland and U.S. Using Dynamic Model Averaging (DMA) and Google Queries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 18-34, July.
    9. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.

    More about this item

    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:wly:jforec:v:34:y:2015:i:8:p:619-648. 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.

    We have no bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.