An artificial recurrent neural network experiment on the future of digital innovation and sustainable economic development in the OECD.

Abstract

The debate on what factors matter for sustaining economic development has been on for many decades. Although there have been disruptions in the technology space, such disruptions do not necessarily inform economic development. It is therefore imperative to examine factors affecting economic development, particularly in light of recovery from losses brought about by the COVID-19 pandemic. This study conducts machine and deep learning experiments to examine how digital innovation can inform economic development going forward. Data used includes 37 members of the organization for economic co operation and development (OECD) from 1990 to 2019. OECD members are global leaders in digital innovation and technology, hence, the choice of this economic and trade bloc. With sufficient historical data on economic growth, capital investments and labour force, in-depth forecasting and analysis are conducted using a range of techniques. While an artificial recurrent neural network experiment is conducted, results are compared with linear and polynomial linear regressions, as well as the prophet model.