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Public sector innovation outcome-driven sustainable development in Bangladesh: applying the dynamic autoregressive distributed lag simulations and Kernel-based regularised least square machine learning algorithm approaches

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  • Islam, Md. Monirul
  • Tareque, Mohammad

Abstract

This research investigates the role of public sector innovation outcomes, e.g. trademark innovation, information and communication technology (ICT), renewable energy, and governance, in the sustainable development of Bangladesh during 1980–2019. Utilising the dynamic autoregressive distributed lag (DARDL) simulation approach, this study divulges a favourable long-term influencing profile of public sector innovation outcomes, i.e. trademark innovation, ICT, and renewable energy on sustainable development, while governance has a heterogeneous impact. Besides, the findings from the DARDL simulations area plots display 10% counterfactual shocks to the public sector innovation outcomes on sustainable development. Furthermore, the Kernel-based regularised least square machine learning algorithm approach used in the study examines the marginal effects of the public sector innovation outcomes on sustainable development for robust findings. Therefore, the policy suggestions are solely concerned with the public sector’s adoption of more innovation dynamics through appropriate policy formulation.

Suggested Citation

  • Islam, Md. Monirul & Tareque, Mohammad, 2023. "Public sector innovation outcome-driven sustainable development in Bangladesh: applying the dynamic autoregressive distributed lag simulations and Kernel-based regularised least square machine learnin," Journal of Public Policy, Cambridge University Press, vol. 43(2), pages 326-357, June.
    • Handle: RePEc:cup:jnlpup:v:43:y:2023:i:2:p:326-357_7
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