Performance Evaluation of Statistical Methods for Forecasting of Unemployment Rate

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F09%3A00011054" target="_blank" >RIV/60076658:12510/09:00011054 - 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

Performance Evaluation of Statistical Methods for Forecasting of Unemployment Rate

Popis výsledku v původním jazyce

The aim of this article is the accuracy evaluation of suitable models used for prediction of the unemployment rate development in Czech Republic under conditions of economic depression. Models were based on exponential smoothing and training of artificial neural networks. The most suitable models, as it was proved two months ago (see [1]), the exponential eventually damped model with additive seasonality and multilayer perceptron forecasted March?s and April?s unemployment rate as 7.58?7.89 % and 7.63?8.33 % for exponential smoothing respective as 7.3?7.45 % and 6.62?8.22 % for multilayer perceptron. Performance of a models measured by Theil?s U were 0.002?0.022 for exponential smoothing respective 0.016?0.041 for multilayer perceptrons. Recalculationof exponential smoothing model and retraining of artificial neural networks on fresh values of the unemployment rate show that 1) smoothing parameters were little modified; 2) same type of ANNs were suitable for solving this problem ? com

Název v anglickém jazyce

Performance Evaluation of Statistical Methods for Forecasting of Unemployment Rate

Popis výsledku anglicky

The aim of this article is the accuracy evaluation of suitable models used for prediction of the unemployment rate development in Czech Republic under conditions of economic depression. Models were based on exponential smoothing and training of artificial neural networks. The most suitable models, as it was proved two months ago (see [1]), the exponential eventually damped model with additive seasonality and multilayer perceptron forecasted March?s and April?s unemployment rate as 7.58?7.89 % and 7.63?8.33 % for exponential smoothing respective as 7.3?7.45 % and 6.62?8.22 % for multilayer perceptron. Performance of a models measured by Theil?s U were 0.002?0.022 for exponential smoothing respective 0.016?0.041 for multilayer perceptrons. Recalculationof exponential smoothing model and retraining of artificial neural networks on fresh values of the unemployment rate show that 1) smoothing parameters were little modified; 2) same type of ANNs were suitable for solving this problem ? com

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

Acta Universitatis Bohemiae Meridionales : vědecký časopis pro ekonomiku, řízení a obchod

ISSN

1212-3285

e-ISSN

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Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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