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

Badi Baltagi (), Georges Bresson () and Jean-Michel Etienne ()
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Jean-Michel Etienne: Department of Economics, Université Paris-Stud 11

No 229, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: This paper 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 fixedand random-coefficients model to estimate this relationship. In particular, this paper uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), we 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 (search for similar items in EconPapers)
JEL-codes: C11 C14 C23 O47 (search for similar items in EconPapers)
Pages: pages
Date: 2020-05
New Economics Papers: this item is included in nep-gro
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Chapter: Growth Empirics: a Bayesian Semiparametric Model With Random Coefficients for a Panel of OECD Countries (2020) Downloads
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