Economics at your fingertips  

Econometric-wavelet prediction in spatial aspect

Monika Hadaś-Dyduch

No 30/2016, Working Papers from Institute of Economic Research

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.

Keywords: prediction; wavelets; wavelet transform (search for similar items in EconPapers)
JEL-codes: F37 C13 G15 (search for similar items in EconPapers)
Date: 2016-06, Revised 2016-06
New Economics Papers: this item is included in nep-ecm and nep-for
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) First version, 2016 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Working Papers from Institute of Economic Research Contact information at EDIRC.
Bibliographic data for series maintained by Adam P. Balcerzak ().

Page updated 2020-03-29
Handle: RePEc:pes:wpaper:2016:no30