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An Economic Growth Model Using Hierarchical Bayesian Method

In: Bayesian Networks - Advances and Novel Applications

Author

Listed:
  • Nur Iriawan
  • Septia Devi Prihastuti Yasmirullah

Abstract

Economic growth can be used as an assessment for the success of the regional economic establishment. Since the Regulation of the Republic Indonesia Number 32 of 2004 has been implemented, the imbalance economic growth among the regencies in Indonesia is rising. The imbalance in the conditions of economic growth differs between regions with the aim of the government to improve social welfare by expanding economic activities in each region. The purpose of this chapter is to elaborate whether there is a difference in economic growth based on the distribution of bank credit for each regency in Indonesia. This research analyzes the economic growth data using hierarchical structure model that follows the normality-based modeling in the first level. The two modeling approaches will be applied, i.e., a general one-level Bayesian approach and a two-level structure hierarchical Bayesian approach. The success of these approaches has demonstrated that the two-level hierarchical structure Bayesian has a better estimation than a general one-level Bayesian. It demonstrates that all of the macro-level characteristics of provinces are significantly influencing the different economic growth in every related province. These variations are also significantly influenced by their cross-level interaction regency and provincial characteristics.

Suggested Citation

  • Nur Iriawan & Septia Devi Prihastuti Yasmirullah, 2019. "An Economic Growth Model Using Hierarchical Bayesian Method," Chapters, in: Douglas McNair (ed.), Bayesian Networks - Advances and Novel Applications, IntechOpen.
  • Handle: RePEc:ito:pchaps:165588
    DOI: 10.5772/intechopen.88650
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    More about this item

    Keywords

    Bayesian; estimation; economic growth; normal distribution; hierarchical;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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