Identification of time-varying model using wavelet approach and AR process

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F15%3APU115327" target="_blank" >RIV/00216305:26220/15:PU115327 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Identification of time-varying model using wavelet approach and AR process

Popis výsledku v původním jazyce

The paper aim is to give recommendation for work with time frequency modeling of macroeconomic time series on the basis of comparative study. We investigate wavelet analysis and time-varying autoregressive process. We follow two main areas of focus - sample size for available data set and its shortening and optimization of parameters of mentioned methods. In case of time-varying autoregressive process we investigate optimization of parameters such as lag length, windowing and overlap. In wavelet analysis approach we investigate the type of wave and scale. Performance of methods is presented on the gross domestic product data of USA, United Kingdom and Korea. These representatives were chosen from the perspective of available sample size and from the reason the country represent economy in different geographic area. An advantage of wavelet analysis is better time resolution. An autoregressive process provides better frequency resolution, but it is quite sensitive to sample size.

Název v anglickém jazyce

Identification of time-varying model using wavelet approach and AR process

Popis výsledku anglicky

The paper aim is to give recommendation for work with time frequency modeling of macroeconomic time series on the basis of comparative study. We investigate wavelet analysis and time-varying autoregressive process. We follow two main areas of focus - sample size for available data set and its shortening and optimization of parameters of mentioned methods. In case of time-varying autoregressive process we investigate optimization of parameters such as lag length, windowing and overlap. In wavelet analysis approach we investigate the type of wave and scale. Performance of methods is presented on the gross domestic product data of USA, United Kingdom and Korea. These representatives were chosen from the perspective of available sample size and from the reason the country represent economy in different geographic area. An advantage of wavelet analysis is better time resolution. An autoregressive process provides better frequency resolution, but it is quite sensitive to sample size.

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

<a href="/cs/project/LO1401" target="_blank" >LO1401: Interdisciplinární výzkum bezdrátových technologií</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

33rd International Conference Mathematical Methods in Economics MME 2015 - Conference Proceedings

ISBN

978-80-261-0539-8

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

665-670

Název nakladatele

University of West Bohemia, Plzeň

Místo vydání

Plzeň

Místo konání akce

Cheb

Datum konání akce

9. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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