IDEAS home Printed from https://ideas.repec.org/p/max/cprwps/229.html
   My bibliography  Save this paper

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

Author

Listed:

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 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.

Suggested Citation

  • Badi Baltagi & Georges Bresson & Jean-Michel Etienne, 2020. "Growth Empirics: A Bayesian Semiparametric Model with Random Coefficients for a Panel of OECD Countries," Center for Policy Research Working Papers 229, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:229
    as

    Download full text from publisher

    File URL: https://surface.syr.edu/cpr/263/
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lazear, Edward P & Rosen, Sherwin, 1981. "Rank-Order Tournaments as Optimum Labor Contracts," Journal of Political Economy, University of Chicago Press, vol. 89(5), pages 841-864, October.
    2. Kolev, Galina V. & Niehues, Judith, 2016. "The inequality-growth relationship: An empirical reassessment," IW-Reports 7/2016, Institut der deutschen Wirtschaft (IW) / German Economic Institute.
    3. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    4. Andrea Bassanini & Stefano Scarpetta, 2001. "Does Human Capital Matter for Growth in OECD Countries?: Evidence from Pooled Mean-Group Estimates," OECD Economics Department Working Papers 282, OECD Publishing.
    5. Badi H. Baltagi & Dong Li, 2002. "Series Estimation of Partially Linear Panel Data Models with Fixed Effects," Annals of Economics and Finance, Society for AEF, vol. 3(1), pages 103-116, May.
    6. Berlemann, Michael & Wesselhöft, Jan-Erik, 2012. "Estimating Aggregate Capital Stocks Using the Perpetual Inventory Method – New Empirical Evidence for 103 Countries –," Working Paper 125/2012, Helmut Schmidt University, Hamburg.
    7. Alesina, Alberto & Perotti, Roberto, 1996. "Income distribution, political instability, and investment," European Economic Review, Elsevier, vol. 40(6), pages 1203-1228, June.
    8. Angel de la Fuente & Rafael Doménech, 2006. "Human Capital in Growth Regressions: How Much Difference Does Data Quality Make?," Journal of the European Economic Association, MIT Press, vol. 4(1), pages 1-36, March.
    9. Neves, Pedro Cunha & Afonso, Óscar & Silva, Sandra Tavares, 2016. "A Meta-Analytic Reassessment of the Effects of Inequality on Growth," World Development, Elsevier, vol. 78(C), pages 386-400.
    10. Enrique Moral-Benito, 2012. "Determinants of Economic Growth: A Bayesian Panel Data Approach," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 566-579, May.
    11. Roberto Cellini, 1997. "Growth empirics: evidence from a panel of annual data," Applied Economics Letters, Taylor & Francis Journals, vol. 4(6), pages 347-351.
    12. Bresson, Georges & Etienne, Jean-Michel & Mohnen, Pierre, 2016. "How Important Is Innovation? A Bayesian Factor-Augmented Productivity Model Based On Panel Data," Macroeconomic Dynamics, Cambridge University Press, vol. 20(8), pages 1987-2009, December.
    13. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    14. Hsiao,Cheng & Pesaran,M. Hashem & Lahiri,Kajal & Lee,Lung Fei (ed.), 1999. "Analysis of Panels and Limited Dependent Variable Models," Cambridge Books, Cambridge University Press, number 9780521631693, September.
    15. Kappler, Marcus, 2004. "Determination of Potential Growth Using Panel Techniques," ZEW Discussion Papers 04-69, ZEW - Leibniz Centre for European Economic Research.
    16. Meryem Duygun & Jiaqi Hao & Anders Isaksson & Robin C. Sickles, 2017. "World Productivity Growth: A Model Averaging Approach," Pacific Economic Review, Wiley Blackwell, vol. 22(4), pages 587-619, October.
    17. Durlauf, Steven N. & Kourtellos, Andros & Minkin, Artur, 2001. "The local Solow growth model," European Economic Review, Elsevier, vol. 45(4-6), pages 928-940, May.
    18. Jiti Gao & Kim Hawthorne, 2006. "Semiparametric estimation and testing of the trend of temperature series," Econometrics Journal, Royal Economic Society, vol. 9(2), pages 332-355, July.
    19. Barro, Robert J & Sala-i-Martin, Xavier, 1997. "Technological Diffusion, Convergence, and Growth," Journal of Economic Growth, Springer, vol. 2(1), pages 1-26, March.
    20. Degui Li & Jia Chen & Jiti Gao, 2011. "Non‐parametric time‐varying coefficient panel data models with fixed effects," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 387-408, October.
    21. N. Gregory Mankiw & David Romer & David N. Weil, 1992. "A Contribution to the Empirics of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(2), pages 407-437.
    22. Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
    23. Boneva, Lena & Linton, Oliver & Vogt, Michael, 2015. "A semiparametric model for heterogeneous panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 327-345.
    24. Li, Qi & Stengos, Thanasis, 1996. "Semiparametric estimation of partially linear panel data models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 389-397.
    25. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    26. Baltagi, Badi H., 2015. "The Oxford Handbook of Panel Data," OUP Catalogue, Oxford University Press, number 9780199940042.
    27. Philippe Aghion & Steven Durlauf (ed.), 2005. "Handbook of Economic Growth," Handbook of Economic Growth, Elsevier, edition 1, volume 1, number 1.
    28. Kunpeng Li & Degui Li & Zhongwen Liang & Cheng Hsiao, 2017. "Estimation of semi-varying coefficient models with nonstationary regressors," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 354-369, March.
    29. Kneip, Alois & Sickles, Robin C. & Song, Wonho, 2012. "A New Panel Data Treatment For Heterogeneity In Time Trends," Econometric Theory, Cambridge University Press, vol. 28(3), pages 590-628, June.
    30. Jianqing Fan & Runze Li, 2004. "New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 710-723, January.
    31. Li, Q. & Hsiao, C., 1998. "Testing serial correlation in semiparametric panel data models," Journal of Econometrics, Elsevier, vol. 87(2), pages 207-237, September.
    32. Baltagi, Badi H. & Li, Qi, 2002. "On instrumental variable estimation of semiparametric dynamic panel data models," Economics Letters, Elsevier, vol. 76(1), pages 1-9, June.
    33. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1.
    34. Jia Chen & Degui Li & Jiti Gao, 2013. "Non- and Semi-Parametric Panel Data Models: A Selective Review," Monash Econometrics and Business Statistics Working Papers 18/13, Monash University, Department of Econometrics and Business Statistics.
    35. Chen, Jia & Gao, Jiti & Li, Degui, 2012. "Semiparametric trending panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 171(1), pages 71-85.
    36. James Honaker & Gary King, 2010. "What to Do about Missing Values in Time‐Series Cross‐Section Data," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 561-581, April.
    37. Hongjun Li & Zhongjian Lin & Cheng Hsiao, 2015. "Testing purchasing power parity hypothesis: a semiparametric varying coefficient approach," Empirical Economics, Springer, vol. 48(1), pages 427-438, February.
    38. Tao Lu, 2017. "Bayesian inference on longitudinal-survival data with multiple features," Computational Statistics, Springer, vol. 32(3), pages 845-866, September.
    39. Lee, Jungyoon & Robinson, Peter, 2015. "Panel nonparametric regression with fixed effects," LSE Research Online Documents on Economics 61431, London School of Economics and Political Science, LSE Library.
    40. Griliches, Zvi, 1980. "R & D and the Productivity Slowdown," American Economic Review, American Economic Association, vol. 70(2), pages 343-348, May.
    41. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.
    42. Lee, Jungyoon & Robinson, Peter M., 2015. "Panel nonparametric regression with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 346-362.
    43. Korobilis, Dimitris & Koop, Gary, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," Essex Finance Centre Working Papers 22665, University of Essex, Essex Business School.
    44. Andrea Bassanini & Stefano Scarpetta & Philip Hemmings, 2001. "Economic Growth: The Role of Policies and Institutions: Panel Data. Evidence from OECD Countries," OECD Economics Department Working Papers 283, OECD Publishing.
    45. Dustin Chambers & Alan Krause, 2010. "Is the relationship between inequality and growth affected by physical and human capital accumulation?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 8(2), pages 153-172, June.
    46. Yangxin Huang & Tao Lu, 2017. "Bayesian inference on partially linear mixed-effects joint models for longitudinal data with multiple features," Computational Statistics, Springer, vol. 32(1), pages 179-196, March.
    47. Fan, Jianqing & Huang, Tao & Li, Runze, 2007. "Analysis of Longitudinal Data With Semiparametric Estimation of Covariance Function," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 632-641, June.
    48. Li, Qi & Hsiao, Cheng & Zinn, Joel, 2003. "Consistent specification tests for semiparametric/nonparametric models based on series estimation methods," Journal of Econometrics, Elsevier, vol. 112(2), pages 295-325, February.
    49. Anders Isaksson & Chenjun Shang & Robin C. Sickles, 2020. "Nonstructural analysis of productivity growth for the industrialized countries: a jackknife model averaging approach," Econometric Reviews, Taylor & Francis Journals, vol. 40(4), pages 321-358, August.
    50. Henderson, Daniel J. & Carroll, Raymond J. & Li, Qi, 2008. "Nonparametric estimation and testing of fixed effects panel data models," Journal of Econometrics, Elsevier, vol. 144(1), pages 257-275, May.
    51. Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
    52. Li, Qi, 1996. "On the root-N-consistent semiparametric estimation of partially linear models," Economics Letters, Elsevier, vol. 51(3), pages 277-285, June.
    53. Jianhua Z. Huang, 2002. "Varying-coefficient models and basis function approximations for the analysis of repeated measurements," Biometrika, Biometrika Trust, vol. 89(1), pages 111-128, March.
    54. Ormerod, J. T. & Wand, M. P., 2010. "Explaining Variational Approximations," The American Statistician, American Statistical Association, vol. 64(2), pages 140-153.
    55. Barro, Robert J, 2000. "Inequality and Growth in a Panel of Countries," Journal of Economic Growth, Springer, vol. 5(1), pages 5-32, March.
    56. Junrong Liu & Robin C. Sickles & E. G. Tsionas, 2017. "Bayesian Treatments for Panel Data Stochastic Frontier Models with Time Varying Heterogeneity," Econometrics, MDPI, vol. 5(3), pages 1-21, July.
    57. Faes, C. & Ormerod, J. T. & Wand, M. P., 2011. "Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 959-971.
    58. Eberhardt, Markus & Bond, Stephen, 2009. "Cross-section dependence in nonstationary panel models: a novel estimator," MPRA Paper 17692, University Library of Munich, Germany.
    59. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    60. Baltagi, Badi H & Griffin, James M, 1988. "A General Index of Technical Change," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 20-41, February.
    61. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
    62. Qi Li & Aman Ullha, 1998. "Estimating partially linear panel data models with one-way error components," Econometric Reviews, Taylor & Francis Journals, vol. 17(2), pages 145-166.
    63. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    64. Liu, Zhenjuan & Stengos, Thanasis, 1999. "Non-linearities in Cross-Country Growth Regressions: A Semiparametric Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 527-538, Sept.-Oct.
    65. Nazrul Islam, 1995. "Growth Empirics: A Panel Data Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(4), pages 1127-1170.
    66. Federico Cingano, 2014. "Trends in Income Inequality and its Impact on Economic Growth," OECD Social, Employment and Migration Working Papers 163, OECD Publishing.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nektarios A. Michail & Konstantinos D. Melas & Dimitris Batzilis, 2021. "Container shipping trade and real GDP growth: A panel vector autoregressive approach," Economics Bulletin, AccessEcon, vol. 41(2), pages 304-315.
    2. Xiaoshi Zhou & Wanglin Ma, 2021. "Does agricultural mechanization reduce vulnerable employment? Evidence from cross-country panel data," Economics Bulletin, AccessEcon, vol. 41(2), pages 294-303.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Georges Bresson & Jean-Michel Etienne & Pierre Mohnen, 2011. "How important is innovation? A Bayesian factor-augmented productivity model on panel data," TEPP Working Paper 2011-06, TEPP.
    2. Markus Eberhardt & Francis Teal, 2008. "Modeling Technology and Technological Change in Manufacturing: How do Countries Differ?," CSAE Working Paper Series 2008-12, Centre for the Study of African Economies, University of Oxford.
    3. Feng, Guohua & Gao, Jiti & Peng, Bin, 2022. "An integrated panel data approach to modelling economic growth," Journal of Econometrics, Elsevier, vol. 228(2), pages 379-397.
    4. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.
    5. Chen, Jia & Li, Degui & Xia, Yingcun, 2019. "Estimation of a rank-reduced functional-coefficient panel data model with serial correlation," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 456-479.
    6. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    7. Michael S. Delgado & Daniel J. Henderson & Christopher F. Parmeter, 2014. "Does Education Matter for Economic Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(3), pages 334-359, June.
    8. Markus Eberhardt & Francis Teal, 2020. "The Magnitude of the Task Ahead: Macro Implications of Heterogeneous Technology," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(2), pages 334-360, June.
    9. Francis Teal & Markus Eberhardt, 2010. "Productivity Analysis in Global Manufacturing Production," Economics Series Working Papers 515, University of Oxford, Department of Economics.
    10. Michael Peneder & Karl Aiginger & Gernot Hutschenreiter & Markus Marterbauer, 2001. "Structural Change and Economic Growth," WIFO Studies, WIFO, number 20668.
    11. Ademola Obafemi Young, 2019. "Growth Impacts of Income Inequality: Empirical Evidence From Nigeria," Research in World Economy, Research in World Economy, Sciedu Press, vol. 10(3), pages 226-262, December.
    12. Zeira, Joseph & di Vaio, Gianfranco & Battisti, Michele, 2013. "Global Divergence in Growth Regressions," CEPR Discussion Papers 9687, C.E.P.R. Discussion Papers.
    13. Markus Eberhardt & Francis Teal, 2010. "Aggregation versus Heterogeneity in Cross-Country Growth Empirics," CSAE Working Paper Series 2010-32, Centre for the Study of African Economies, University of Oxford.
    14. Josip Tica & Viktor Viljevac & Matija Matiæ, 2023. "Employment rate and economic growth:The case of transition countries," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 41(1), pages 9-39.
    15. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers 02/13, Institute for Fiscal Studies.
    16. Nikos Benos & Stelios Karagiannis, 2018. "Inequality And Growth In The United States: Why Physical And Human Capital Matter," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 572-619, January.
    17. Jia Chen & Jiti Gao, 2014. "Semiparametric Model Selection in Panel Data Models with Deterministic Trends and Cross-Sectional Dependence," Monash Econometrics and Business Statistics Working Papers 15/14, Monash University, Department of Econometrics and Business Statistics.
    18. Boneva, Lena & Linton, Oliver & Vogt, Michael, 2015. "A semiparametric model for heterogeneous panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 327-345.
    19. Lee, Yoon-Jin, 2014. "Testing a linear dynamic panel data model against nonlinear alternatives," Journal of Econometrics, Elsevier, vol. 178(P1), pages 146-166.
    20. Naima Chrid & Sami Saafi & Mohamed Chakroun, 2021. "Export Upgrading and Economic Growth: a Panel Cointegration and Causality Analysis," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(2), pages 811-841, June.

    More about this item

    Keywords

    GDP Per Capita; Growth Empirics; Mean Field Variational Bayes Approximation; Panel Data; Random Coefficients; Semiparametric Model;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:max:cprwps:229. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Katrina Fiacchi (email available below). General contact details of provider: https://edirc.repec.org/data/cpsyrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.