Revisiting Growth Empirics Based on IV Panel Quantile Regression

Lijuan Huo, Tae-Hwan Kim () and Yunmi Kim
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Lijuan Huo: Beijing Institute of Technology
Yunmi Kim: University of Seoul

No 2014rwp-72, Working papers from Yonsei University, Yonsei Economics Research Institute

Abstract: We analyze the well-known issue of economic growth convergence using quantile regres- sion. 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 conver- gence speed changed in a low growth period such as the ?Great Recession,?that started in 2008? We propose using IV panel quantile regression to answer the question. Our empirical ?ndings 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.

Keywords: Quantile regression; panel data; endogeneity; growth convergence. (search for similar items in EconPapers)
JEL-codes: C2 O4 (search for similar items in EconPapers)
Pages: 20pages
Date: 2014-12
New Economics Papers: this item is included in nep-fdg and nep-gro
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Journal Article: Revisiting growth empirics based on IV panel quantile regression (2015)
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