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Revisiting growth empirics based on IV panel quantile regression

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  • Lijuan Huo
  • Tae-Hwan Kim
  • Yunmi Kim

Abstract

We analyse the well-known issue of economic growth convergence using quantile regression. Most previous studies have used a least squares (LS) method or variation, which focuses on the issue only at the mean of the growth rate. Therefore, such results cannot provide a satisfactory answer to what can happen if the growth rate is far from the conditional mean level. For example, we consider the following question: do we still have economic growth convergence or is the convergence speed changed in a low growth period such as the 'Great Recession,' that started in 2008? We propose using instrumental variable panel quantile regression to answer this question. Our empirical findings demonstrate that economic growth convergence occurs at all quantiles over the entire conditional distribution, but that the convergence speed does depend on quantiles; the convergence speed is much higher when the GDP growth rate is at either high or low quantiles.

Suggested Citation

  • Lijuan Huo & Tae-Hwan Kim & Yunmi Kim, 2015. "Revisiting growth empirics based on IV panel quantile regression," Applied Economics, Taylor & Francis Journals, vol. 47(36), pages 3859-3873, August.
  • Handle: RePEc:taf:applec:v:47:y:2015:i:36:p:3859-3873
    DOI: 10.1080/00036846.2015.1019038
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    References listed on IDEAS

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    JEL classification:

    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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