Growth Empirics: a Bayesian Semiparametric Model With Random Coefficients for a Panel of OECD Countries

Badi Baltagi, Georges Bresson and Jean-Michel Etienne

A chapter in Essays in Honor of Cheng Hsiao, 2020, vol. 41, pp 217-253 from Emerald Group Publishing Limited

Abstract: This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

Keywords: GDP per capita; growth empirics; mean field variational Bayes approximation; panel data; random coefficients; semiparametric model; C11; C14; C23; O47 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Working Paper: Growth Empirics: A Bayesian Semiparametric Model with Random Coefficients for a Panel of OECD Countries (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320200000041007

DOI: 10.1108/S0731-905320200000041007

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