Document Type

Working Paper




GDP Per Capita, Growth Empirics, Mean Field Variational Bayes Approximation, Panel Data, Random Coefficients, Semiparametric Model




This paper is written in honor of Professor Cheng Hsiao's valuable contributions to econometrics. We thank the editors M. Hashem Pesaran, Tong Li, Dek Terrell, an anonymous referee, Thomas Fomby, Robin Sickles and the participants of the Advances in Econometrics Conference in honor of Cheng Hsiao (LSU, October 26-28, 2018) for their valuable comments and suggestions, which were very helpful for revising and improving our manuscript. We are also grateful to participants of the 23th International Conference on Panel Data (IPDC2017, Thessaloniki, July 7-8 2017) and especially Almas Heshmati for valuable comment. We thank Jean-Etienne Carlotti for providing us the OECD datasets. The usual disclaimers apply.


Economic Policy | Economics | Public Affairs, Public Policy and Public Administration


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 fixed and 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.



Additional Information

Working paper no. 229


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.



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