Combining forecasts to enhance fish production prediction: The case of coastal fish production in Morocco

Artikel

This paper seeks to enhance forecast accuracy by combining three individual forecasting models. These models include: the Autoregressive Integrated Moving Average model (ARIMA), the Generalized Autoregressive Conditional Heteroscedastic model (GARCH), and the Census X11 model. Applied to the Moroccan coastal fish production, the empirical results show that in terms of predictive ability the composite model outperforms the individual forecasting models. In addition, the results reveal that the forecast accuracy gains arising from combining the individual forecasts range from nearly 8% to over 95%.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Atlantic Review of Economics ; ISSN: 2174-3835 ; Volume: 2 ; Year: 2015 ; Pages: 1-19 ; A Coruña: Colegio de Economistas de A Coruña

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; Econometric Modeling: Other ; Renewable Resources and Conservation: Fishery; Aquaculture
• Subject: forecasting ; composite model ; fish production

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Bouras, David
• Event: Veröffentlichung
• (who): Colegio de Economistas de A Coruña
• (where): A Coruña
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/146603
• Last update: 10.03.2025, 11:41 AM CET