Exports and imports in Zimbabwe: recent insights from artificial neural networks

Thabani Nyoni

MPRA Paper from University Library of Munich, Germany

Abstract: This study, which is the first of its kind in the case of Zimbabwe; attempts to model and forecast Zimbabwe’s exports and imports using annual time series data ranging over the period 1975 – 2017. In order to analyze Zimbabwe’s export and import dynamics, the study employed the Neural Network approach, a deep-learning technique which has not been applied in this area in the case of Zimbabwe. The Hyperbolic Tangent function was selected and applied as the activation function of the neural networks applied in this study. The neural networks applied in this research were evaluated using the most common forecast evaluation statistics, i.e. the Error, MSE and MAE; and it was clearly shown that the neural networks yielded reliable forecasts of Zimbabwe’s exports and imports over the period 2018 – 2027. The main results of the study indicate that imports will continue to outperform exports over the out-of-sample period. Amongst other policy recommendations, the study encourages Zimbabwean policy makers to intensify export growth promotion policies and strategies such as clearly identifying export drivers as well as export diversification if persistant current account deficits in Zimbabwe are to be dealt with effectively.

Keywords: ANNs; exports; forecast; hyperbolic tangent function; imports; trade deficits; Zimbabwe (search for similar items in EconPapers)
JEL-codes: F13 P33 Q17 (search for similar items in EconPapers)
Date: 2019-11-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-int and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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