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Econometric-wavelet prediction in spatial aspect


  • Monika Hadas-Dyduch

    () (University of Economics in Katowice)


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

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    More about this item


    prediction; wavelets; wavelet transform;

    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

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