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Mamadou Michel Diakhate and Seydi Ababacar Dieng
''Forecasting Senegalese quarterly GDP per capita using recurrent neural network''
( 2022, Vol. 42 No.4 )
This article evaluates the predictive efficiency of RNNs comparing two types of architecture on quarterly GDP per capita data from Senegal over the period 1960-2020, namely a recursive neural network with re-estimation and a recursive neural network without re- estimate. The RMSE, MAPE and MAE values of the chosen neural network are respectively 7.41%, 8% and 7.73% lower than those of the RNN model has one hidden layer without re-estimation. Indeed, the architecture with two hidden layers converges less quickly than that with only one hidden layer. Thus, the one hidden layer RNN with re-estimate remains the best forecast of Senegal's quarterly GDP per capita during the test period considered. These results suggest the use of artificial neural networks for forecasting economic variables.
Keywords: Recurrent Neural Network (RNN); Estimate; forecasting; GDP per capita; Senegal.
JEL: C4 - Econometric and Statistical Methods: Special Topics
E3 - Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data)
Manuscript Received : May 10 2022 Manuscript Accepted : Dec 30 2022

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