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Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies

Antonio Andres, Abraham Otero and Voxi Heinrich Amavilah ()

MPRA Paper from University Library of Munich, Germany

Abstract: Missing values and the inconsistency of the measures of the knowledge economy remain vexing problems that hamper policy-making and future research in developing and emerging economies. This paper contributes to the new and evolving literature that seeks to advance better understanding of the importance of the knowledge economy for policy and further research in developing and emerging economies. In this paper we use a supervised machine deep learning neural network (DLNN) approach to predict the knowledge economy index of 71 developing and emerging economies during the 1995-2017 period. Applied in combination with a data imputation procedure based on the K-closest neighbor algorithm, DLNN is capable of handling missing data problems better than alternative methods. A 10-fold validation of the DLNN yielded low quadratic and absolute error (0,382 +- 0,065). The results are robust and efficient, and the model’s predictive power is high. There is a difference in the predictive power when we disaggregate countries in all emerging economies versus emerging Central European countries. We explain this result and leave the rest to future endeavors. Overall, this research has filled in gaps due to missing data thereby allowing for effective policy strategies. At the aggregate level development agencies, including the World Bank that originated the KEI, would benefit from our approach until substitutes come along.

Keywords: Machine deep learning neural networks; developing economies, emerging economies, knowledge economy, knowledge economy index, World Bank (search for similar items in EconPapers)
JEL-codes: C45 C53 O38 O41 O57 P41 (search for similar items in EconPapers)
Date: 2021-04-15
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-isf and nep-knm
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