Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies
نویسندگان
چکیده
• Inconsistency of the knowledge economy (KE) measures remains a vexing problem. World Bank’s KE index (KEI) is useful tool for measuring economy. KEI often missing developing countries due to lack data on which it based. A deep learning neural network was trained predict KEI, even with data. The relative prediction error available 2.99% Missing values and inconsistency remain problems that hamper policy-making future research in emerging economies. This paper contributes new evolving literature seeks advance better understanding importance policy further In this we use supervised machine (DLNN) approach 71 economies during 1995–2017 period. Applied combination imputation procedure based K-closest neighbor algorithm, DLNN capable handling than alternative methods. 10-fold validation yielded low quadratic absolute (0,382 +- 0,065). results are robust efficient, model’s predictive power high. There difference when disaggregate all versus Central European countries. We explain result leave rest endeavors. Overall, has filled gaps thereby allowing effective strategies. At aggregate level development agencies, including Bank originated would benefit from our until substitutes come along.
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ژورنال
عنوان ژورنال: Expert Systems With Applications
سال: 2021
ISSN: ['1873-6793', '0957-4174']
DOI: https://doi.org/10.1016/j.eswa.2021.115514