Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021-2022
Abstract: Unemployment has risen as the economy has shrunk. The coronavirus crisis has affected many sectors in Romania, some companies diminishing or even ceasing their activity. Making forecasts of the unemployment rate has a fundamental impact and importance on future social policy strategies. The aim of the paper is to comparatively analyze the forecast performances of different univariate time series methods with the purpose of providing future predictions of unemployment rate. In order to do that, several forecasting models (seasonal model autoregressive integrated moving average (SARIMA), self-exciting threshold autoregressive (SETAR), Holt-Winters, ETS (error, trend, seasonal), and NNAR (neural network autoregression)) have been applied, and their forecast performances have been evaluated on both the in-sample data covering the period January 2000 - December 2017 used for the model identification and estimation and the out-of-sample data covering the last three years, 2018-2020. The
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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Veröffentlichungsversion
begutachtet (peer reviewed)
In: Entropy ; 23 (2021) 3 ; 1-31
- Classification
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Wirtschaft
- Event
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Veröffentlichung
- (where)
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Mannheim
- (who)
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SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
- (when)
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2021
- Creator
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Davidescu, Adriana AnaMaria
Apostu, Simona-Andreea
Paul, Andreea
- DOI
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10.3390/e23030325
- URN
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urn:nbn:de:0168-ssoar-79412-5
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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25.03.2025, 1:56 PM CET
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Davidescu, Adriana AnaMaria
- Apostu, Simona-Andreea
- Paul, Andreea
- SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
Time of origin
- 2021
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