IDEAS home Printed from https://ideas.repec.org/a/alu/journl/v2y2011i13p31.html
   My bibliography  Save this article

The Assessement Of Uncertainty In Predictions Determined By The Variables Aggregation

Author

Listed:
  • Mihaela Bratu

    (Academy of Economic Studies Bucharest)

Abstract

The aggregation of the variables that compose an indicator, as GDP, which should beforecasted, is not mentioned explicitly in literature as a source of forecasts uncertainty. In thisarticle we demonstrate that variables aggregation is an important source of uncertainty inforecasting and we evaluate the accuracy of predictions for a variable obtained by aggregationusing two different strategies. Actually, the accuracy is an important dimension of uncertainty. Inthis study based on data on U.S. GDP and its components in 1995-2010, we found that GDP one-step-ahead forecasts made by aggregating the components with variable weights, modeled usingARMA procedure, have a higher accuracy than those with constant weights or the direct forecasts.Excepting the GDP forecasts obtained directly from the model, the one-step-ahead forecastsresulted form the GDP components‘ forecasts aggregation are better than those made on anhorizon of 3 years . The evaluation of this source of uncertainty should be considered formacroeconomic aggregates in order to choose the most accurate forecast.

Suggested Citation

  • Mihaela Bratu, 2011. "The Assessement Of Uncertainty In Predictions Determined By The Variables Aggregation," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(13), pages 1-31.
  • Handle: RePEc:alu:journl:v:2:y:2011:i:13:p:31
    as

    Download full text from publisher

    File URL: http://oeconomica.uab.ro/upload/lucrari/1320112/31.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. 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.
    2. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
    3. George Athanasopoulos & Farshid Vahid, 2008. "A complete VARMA modelling methodology based on scalar components," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 533-554, May.
    4. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    5. David F. Hendry & Kirstin Hubrich, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 216-227, April.
    6. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    7. Marco Vega, 2004. "Policy Makers Priors and Inflation Density Forecasts," Econometrics 0403005, EconWPA.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    source of uncertainty; forecasts; accuracy; disaggregation over variables; strategy of prediction; DM test;

    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:alu:journl:v:2:y:2011:i:13:p:31. 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: (Dan-Constantin Danuletiu). 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.

    If CitEc recognized a 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.

    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.