Revisiting growth empirics based on IV panel quantile regression

Lijuan Huo, Tae-Hwan Kim () and Yunmi Kim

Applied Economics, 2015, vol. 47, issue 36, 3859-3873

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.

Date: 2015
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DOI: 10.1080/00036846.2015.1019038

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