Forecasting Moldova’s monthly exports using autoregressive models with seasonal dummies
Exports significantly influence Moldova’s economic stability and regional integration, particularly due to its agriculture-driven economy characterized by pronounced seasonality. This study evaluates the forecasting accuracy of autoregressive integrated moving average (ARIMA) models, enhanced with s...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Cogent Business & Management |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311975.2025.2519988 |
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| _version_ | 1849421995473633280 |
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| author | Zionovia Toaca Liliana Staver Alexandru Stratan Viorica Lopotenco Victoria Cociug |
| author_facet | Zionovia Toaca Liliana Staver Alexandru Stratan Viorica Lopotenco Victoria Cociug |
| author_sort | Zionovia Toaca |
| collection | DOAJ |
| description | Exports significantly influence Moldova’s economic stability and regional integration, particularly due to its agriculture-driven economy characterized by pronounced seasonality. This study evaluates the forecasting accuracy of autoregressive integrated moving average (ARIMA) models, enhanced with seasonal dummy variables, for predicting Moldova’s monthly exports. It systematically compares traditional ARIMA methods, ARIMA with seasonal dummies, and modern techniques such as Gated Recurrent Unit (GRU) neural networks. Forecast performance is assessed using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Theil’s U-statistic, and the Cost-Value Ratio (CVR). Results demonstrate that combining forecasts from multiple ARIMA models significantly improves predictive accuracy, with weighted combinations based on MAPE reducing forecast errors by approximately 10%. The findings highlight the effectiveness and adaptability of combined forecasting models, emphasizing their value for small, open economies like Moldova’s that are susceptible to external shocks and seasonal fluctuations. This research provides robust empirical evidence supporting the adoption of combined econometric forecasting methodologies in economic policymaking and strategic planning. |
| format | Article |
| id | doaj-art-4de2761df9ff427d9d27aefbbe266ed4 |
| institution | Kabale University |
| issn | 2331-1975 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Business & Management |
| spelling | doaj-art-4de2761df9ff427d9d27aefbbe266ed42025-08-20T03:31:19ZengTaylor & Francis GroupCogent Business & Management2331-19752025-12-0112110.1080/23311975.2025.2519988Forecasting Moldova’s monthly exports using autoregressive models with seasonal dummiesZionovia Toaca0Liliana Staver1Alexandru Stratan2Viorica Lopotenco3Victoria Cociug4Academy of Economic Studies of Moldova, Chisinau, Moldova, Republic of MoldovaAcademy of Economic Studies of Moldova, Chisinau, Moldova, Republic of MoldovaAcademy of Economic Studies of Moldova, Chisinau, Moldova, Republic of MoldovaAcademy of Economic Studies of Moldova, Chisinau, Moldova, Republic of MoldovaAcademy of Economic Studies of Moldova, Chisinau, Moldova, Republic of MoldovaExports significantly influence Moldova’s economic stability and regional integration, particularly due to its agriculture-driven economy characterized by pronounced seasonality. This study evaluates the forecasting accuracy of autoregressive integrated moving average (ARIMA) models, enhanced with seasonal dummy variables, for predicting Moldova’s monthly exports. It systematically compares traditional ARIMA methods, ARIMA with seasonal dummies, and modern techniques such as Gated Recurrent Unit (GRU) neural networks. Forecast performance is assessed using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Theil’s U-statistic, and the Cost-Value Ratio (CVR). Results demonstrate that combining forecasts from multiple ARIMA models significantly improves predictive accuracy, with weighted combinations based on MAPE reducing forecast errors by approximately 10%. The findings highlight the effectiveness and adaptability of combined forecasting models, emphasizing their value for small, open economies like Moldova’s that are susceptible to external shocks and seasonal fluctuations. This research provides robust empirical evidence supporting the adoption of combined econometric forecasting methodologies in economic policymaking and strategic planning.https://www.tandfonline.com/doi/10.1080/23311975.2025.2519988ARIMA modelscombined forecastingforecasting accuracyseasonal dummiesstationarity testTime series analysis |
| spellingShingle | Zionovia Toaca Liliana Staver Alexandru Stratan Viorica Lopotenco Victoria Cociug Forecasting Moldova’s monthly exports using autoregressive models with seasonal dummies Cogent Business & Management ARIMA models combined forecasting forecasting accuracy seasonal dummies stationarity test Time series analysis |
| title | Forecasting Moldova’s monthly exports using autoregressive models with seasonal dummies |
| title_full | Forecasting Moldova’s monthly exports using autoregressive models with seasonal dummies |
| title_fullStr | Forecasting Moldova’s monthly exports using autoregressive models with seasonal dummies |
| title_full_unstemmed | Forecasting Moldova’s monthly exports using autoregressive models with seasonal dummies |
| title_short | Forecasting Moldova’s monthly exports using autoregressive models with seasonal dummies |
| title_sort | forecasting moldova s monthly exports using autoregressive models with seasonal dummies |
| topic | ARIMA models combined forecasting forecasting accuracy seasonal dummies stationarity test Time series analysis |
| url | https://www.tandfonline.com/doi/10.1080/23311975.2025.2519988 |
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