Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F21%3A10247636" target="_blank" >RIV/61989100:27510/21:10247636 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0957417421009246?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417421009246?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2021.115514" target="_blank" >10.1016/j.eswa.2021.115514</a>

Result language

angličtina

Original language name

Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies

Original language description

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&apos;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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50203 - Industrial relations

Project

<a href="/en/project/GA19-25280S" target="_blank" >GA19-25280S: Drivers and impacts of the technological knowledge in emerging market and developing economies</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

—

Volume of the periodical

184

Issue of the periodical within the volume

december

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

115514

UT code for WoS article

000697925100004

EID of the result in the Scopus database

2-s2.0-85110237943