IDEAS home Printed from https://ideas.repec.org/p/pes/wpaper/2016no30.html
   My bibliography  Save this paper

Econometric-wavelet prediction in spatial aspect

Author

Listed:
  • Monika Hadas-Dyduch

    (University of Economics in Katowice)

Abstract

The aim of this article is the prediction of GDP Polish and other selected European countries. For this purpose integrated into one algorithm econometric methods and wavelet analysis. Econometric methods and wavelet transform are combined goal of constructing a copyright model for predicting macroeconomic indicators. In the article, for estimating the macroeconomic indicators on the example of GDP proposed authorial algorithm that combines the following methods: a method trend creep method of alignment exponential and analysis multiresolution. Used econometric methods, this is a trend crawling and alignment exponential have been modified in several major stages. The aim of the merger of these methods is the construction of algorithm to predict short-term time series. In the copyright algorithm was applied wavelet continuous compactly supported. wavelet used Daubechies. The Daubechies wavelets, are a family of orthogonal wavelets and characterized by a maximal number of vanishing moments for some given support. With each wavelet type of this class, there is a scaling function which generates an orthogonal multiresolution analysis.

Suggested Citation

  • Monika Hadas-Dyduch, 2016. "Econometric-wavelet prediction in spatial aspect," Working Papers 30/2016, Institute of Economic Research, revised Jun 2016.
    • Handle: RePEc:pes:wpaper:2016:no30
    as

    Download full text from publisher

    File URL: http://www.badania-gospodarcze.pl/images/Working_Papers/2016_No_30.pdf
    File Function: First version, 2016
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    prediction; wavelets; wavelet transform;
    All these keywords.

    JEL classification:

    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pes:wpaper:2016:no30. 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: Adam P. Balcerzak (email available below). General contact details of provider: https://edirc.repec.org/data/ibgtopl.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.