Finding Chaos in Finnish GDP

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00223466" target="_blank" >RIV/68407700:21230/14:00223466 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s11633-014-0785-6" target="_blank" >https://doi.org/10.1007/s11633-014-0785-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11633-014-0785-6" target="_blank" >10.1007/s11633-014-0785-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Finding Chaos in Finnish GDP

Popis výsledku v původním jazyce

The goal of this paper is to analyze the Finnish gross domestic product (GDP) and to find chaos in the Finnish GDP. We chose Finland where data has been available since 1975, because we needed the longest time series possible. At first we estimated the time delay and the embedding dimension, which is needed for the Lyapunov exponent estimation and for the phase space reconstruction. Subsequently, we computed the largest Lyapunov exponent, which is one of the important indicators of chaos. Then we calculated the 0-1 test for chaos. Finally we computed the Hurst exponent by rescaled range analysis and by dispersional analysis. The Hurst exponent is a numerical estimate of the predictability of a time series. In the end, we executed a recurrent analysis and displayed recurrence plots of detrended GDP time series. The results indicated that chaotic behaviors obviously exist in GDP.

Název v anglickém jazyce

Finding Chaos in Finnish GDP

Popis výsledku anglicky

The goal of this paper is to analyze the Finnish gross domestic product (GDP) and to find chaos in the Finnish GDP. We chose Finland where data has been available since 1975, because we needed the longest time series possible. At first we estimated the time delay and the embedding dimension, which is needed for the Lyapunov exponent estimation and for the phase space reconstruction. Subsequently, we computed the largest Lyapunov exponent, which is one of the important indicators of chaos. Then we calculated the 0-1 test for chaos. Finally we computed the Hurst exponent by rescaled range analysis and by dispersional analysis. The Hurst exponent is a numerical estimate of the predictability of a time series. In the end, we executed a recurrent analysis and displayed recurrence plots of detrended GDP time series. The results indicated that chaotic behaviors obviously exist in GDP.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

50201 - Economic Theory

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

International Journal of Automation and Computing

ISSN

1476-8186

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CN - Čínská lidová republika

Počet stran výsledku

10

Strana od-do

231-240

Kód UT WoS článku

000442380700001

EID výsledku v databázi Scopus

2-s2.0-84902317892