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A simulation experiment on ICT and patent intensity in South Africa: An application of the novel dynamic ARDL machine learning model

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  • Adedoyin, Festus Fatai
  • Mavengere, Nicholas
  • Mutanga, Alfred

Abstract

The aim of this study is to examine the effect of shocks to patent intensity and its empirical and practical policy implications for the South African economy. This stems from the gap in the literature on policy simulation exercises related to the boost in Information and Communications Technology (ICT) and patent intensity in African countries. Hence, this study established the dynamic relationship between patent intensity and economic growth in South Africa for the period of 1980–2020, alongside essential macroeconomic variables such as government expenditure, gross fixed capital formation, labour force, and trade. We use the Autoregressive distributed lag model (ARDL) to capture short-run and long-run relationships, novel dynamic ARDL and Kernel-based Regularized Least Squares (KRLS) to capture the counterfactual shocks in the economic growth. The ARDL result revealed that government expenditure, labour force, and trade openness significantly foster economic growth in the long-run and short-run. Also, while patent intensity and gross fixed capital formation increase the economy in the long-run and short-run, their interaction term significantly diminishes the growth. Further in the analysis is the dynamic ARDL simulation and KRLS, which predicted the counterfactual shocks of economic growth based on a + 26 % change in patent intensity. The result showed that the increasing volume of patent intensity first has a low effect on South Africa economic growth, but later rebound upwardly, thus indicating that change in patent intensity has a long-lasting impact on sustainable economic growth. The direction that is useful for policy is also highlighted and discussed.

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  • Adedoyin, Festus Fatai & Mavengere, Nicholas & Mutanga, Alfred, 2022. "A simulation experiment on ICT and patent intensity in South Africa: An application of the novel dynamic ARDL machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    • Handle: RePEc:eee:tefoso:v:185:y:2022:i:c:s0040162522005650
    DOI: 10.1016/j.techfore.2022.122044
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    References listed on IDEAS

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