Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/3/325 |
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| author | Adriana AnaMaria Davidescu Simona-Andreea Apostu Andreea Paul |
| author_facet | Adriana AnaMaria Davidescu Simona-Andreea Apostu Andreea Paul |
| author_sort | Adriana AnaMaria Davidescu |
| collection | DOAJ |
| description | 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 forecast of unemployment rate relies on the next two years, 2021–2022. Based on the in-sample forecast assessment of different methods, the forecast measures root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) suggested that the multiplicative Holt–Winters model outperforms the other models. For the out-of-sample forecasting performance of models, RMSE and MAE values revealed that the NNAR model has better forecasting performance, while according to MAPE, the SARIMA model registers higher forecast accuracy. The empirical results of the Diebold–Mariano test at one forecast horizon for out-of-sample methods revealed differences in the forecasting performance between SARIMA and NNAR, of which the best model of modeling and forecasting unemployment rate was considered to be the NNAR model. |
| first_indexed | 2024-03-10T13:24:01Z |
| format | Article |
| id | doaj.art-421a79cf5fba496f8283fa3f34191134 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T13:24:01Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-421a79cf5fba496f8283fa3f341911342023-11-21T09:46:31ZengMDPI AGEntropy1099-43002021-03-0123332510.3390/e23030325Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022Adriana AnaMaria Davidescu0Simona-Andreea Apostu1Andreea Paul2Department of Statistics and Econometrics, Bucharest University of Economic Studies, 010552 Bucharest, RomaniaDepartment of Statistics and Econometrics, Bucharest University of Economic Studies, 010552 Bucharest, RomaniaDepartment of International Economic Relations, Bucharest University of Economic Studies, 15–17 Dorobanti St., Sector 1, 010404 Bucharest, RomaniaUnemployment 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 forecast of unemployment rate relies on the next two years, 2021–2022. Based on the in-sample forecast assessment of different methods, the forecast measures root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) suggested that the multiplicative Holt–Winters model outperforms the other models. For the out-of-sample forecasting performance of models, RMSE and MAE values revealed that the NNAR model has better forecasting performance, while according to MAPE, the SARIMA model registers higher forecast accuracy. The empirical results of the Diebold–Mariano test at one forecast horizon for out-of-sample methods revealed differences in the forecasting performance between SARIMA and NNAR, of which the best model of modeling and forecasting unemployment rate was considered to be the NNAR model.https://www.mdpi.com/1099-4300/23/3/325unemployment rateSARIMASETARHolt–WintersETSneural network autoregression |
| spellingShingle | Adriana AnaMaria Davidescu Simona-Andreea Apostu Andreea Paul Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022 Entropy unemployment rate SARIMA SETAR Holt–Winters ETS neural network autoregression |
| title | Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022 |
| title_full | Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022 |
| title_fullStr | Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022 |
| title_full_unstemmed | Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022 |
| title_short | Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022 |
| title_sort | comparative analysis of different univariate forecasting methods in modelling and predicting the romanian unemployment rate for the period 2021 2022 |
| topic | unemployment rate SARIMA SETAR Holt–Winters ETS neural network autoregression |
| url | https://www.mdpi.com/1099-4300/23/3/325 |
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